This report presents the methodology and results of assessing the impacts of petroleum reduction strategies on the California economy. Methodology is discussed first, then results. The methodology employed is computable general equilibrium (CGE) modeling. CGE models are designed to captures the fundamental economic relationships between producers, consumers, and government. The models are “computable” because numeric solutions are found using computers rather than solved for algebraically. They are “general” in the sense that all markets and all income flows in the economy are accounted for. They reflect “equilibrium” insofar as prices adjust to equilibrate the demand for and supply of goods, services, and factors of production (labor and capital) the model. The specific model employed here is a modified version of E-DRAM (Environmental-Dynamic Revenue Analysis Model). E-DRAM was built for the California Environmental Protection Agency's Air Resources Board (ARB) by researchers at the University of California, Berkeley (UCB). E-DRAM evolved from DRAM (Dynamic Revenue Analysis Model), which was developed jointly by the California Department of Finance (DOF) and Berkeley researchers to perform dynamic revenue analyses of proposed legislation as mandated by California State Senate bill 1837 in 1994. Much of the description of E-DRAM below is closely adapted from Berck, et. al. (Summer 1996), which henceforth will be referred to as the DRAM Report.
2 The remainder of this introduction is a non-technical description of E-DRAM. Section 6.2 outlines modifications made to E-DRAM for this project. Section 6.3 presents baseline solutions to the model for the years 1999, 2020, and 2050. Section 6.4 evaluates various policy scenarios in 2020 and 2050. Section 6.5 analyses the sensitivity of the results to select model parameters. Section 6.6 offers concluding remarks.
E-DRAM describes the relationship among California producers, California households, California governments, and the rest of the world. Rather than tracking each individual producer, household, or government agency in the economy, however, E-DRAM combines similar agents into single sectors. Constructing a cogent sectoring scheme, the first step of model construction, is discussed immediately below; this discussion is followed by a description of the key agents in the economy – producers and consumers.
E-DRAM, like all other empirical economic models, treats aggregates rather than individual agents. This is done both to provide focus for the analysis and contain the number of variables in the model. Constructing a cogent aggregation (or sectoring) scheme is critical in the development of a CGE model because it determines the flows that the model will be able to trace explicitly. For the E-DRAM model, the California economy has been divided into 93 distinct sectors: 29 industrial sectors, 2 factor sectors (labor and capital), 9 consumer good sectors, 7 household sectors, 1 investment sector, 45 government sectors, and one sector representing the rest of the world. The complete details of the sectoring are given in Chapter II of the DRAM Report. For industrial sectoring purposes, all California firms making similar products are lumped together. The agriculture sector, for example, contains all California firms producing agricultural products. The output 2 The DRAM Report, "Dynamic Revenue Analysis for California" (Berck, et. al., Summer 1996), is available at www.dof.ca.gov/HTML/FS_DATA/dyna-rev/dynrev.htm
. .

value of that sector is the value of all crops produced by California growers. A sector's labor demand is the sum of labor used by all firms in the sector. Along with agriculture, there are 28 other producer aggregates in the model. These aggregates generally represent the major industrial and commercial sectors of the California economy, though a few are tailored to capture sectors of particular regulatory interest. For instance, production of internal combustion engines and consumer chemicals are each delineated as distinct sectors at the request of ARB.
3 Data for the industrial sectors originates from the U.S. Department of Commerce's Bureau of Economic Analysis (BEA), and is based on the Census of Business – a detailed survey of U.S. companies conducted every five years.
4 The survey contains information about intermediate purchases, factor (labor, capital, land and entrepreneurship) payments, and taxes. Although quite extensive, the survey only allows inference about groups of firms at the national level. The conversion of national data to updated California data is accomplished using a combination of state level employment data and estimates from DOF's econometric modeling. Like firms, households are also aggregated. California households are divided into categories based upon their income. There are seven such categories in the model, each one corresponding to a California Personal Income Tax marginal tax rate (0, 1, 2, 4, 6, 8, and 9.3 percent). Thus, the income from all households in the one-percent bracket is added together and becomes the income for the “one-percent” household sector. Similarly, all expenditure on agricultural goods by the one-percent households is added and becomes the expenditure of the one-percent household sector on agricultural goods. Total household expenditure on agricultural goods is the sum of expenditures by all seven household sectors. Household income data come from the California Franchise Tax Board Personal Income Tax "sanitized" sample. Data on consumption by income class is derived from national survey data. The government sectors in DRAM are organized so that both government revenue flows and expenditure flows are traced explicitly. The DRAM includes 45 government sectors: 7 federal, 27 state, and 11 local. Government sector data is culled from published federal, state, and local government reports.
Fundamental to the California economy, and hence E-DRAM, are the relationships between the two principal types of economic agents – producers and households. Producers, also known as firms, are aggregated into industrial sectors, and each sector is modeled as a competitive firm. For instance, the output of all of California’s agricultural firms is modeled as coming from a single entity, the agriculture sector. Each sector takes the price that it receives for its output and the prices that it pays for its inputs (capital and labor, called “factors of production,” and other inputs, called “intermediate goods”) as fixed. This is the competitive model: producers do not believe that their decisions have any effect on prices. Each producer is assumed to choose inputs and output to maximize profits. Inputs are labor, capital, and intermediate goods (outputs of other firms). Thus, the producer’s supply of output is a function of price and the producer’s demand for inputs is a function of price. More information on producers is provided in Chapter IV of the DRAM Report. Households make two types of decisions: they decide to buy goods and services; they also decide to sell labor and capital services. They are assumed to make these decisions in the way that maximizes their happiness (called “utility” in the economics literature). Like firms, they take the prices of the goods that they buy and the wage of the labor that they sell as fixed. In addition to their labor income, households receive dividends and interest from their stocks and bonds and other ownership interests in capital. 3 The alcohol, tobacco, and horse racing sector, distinct in DRAM, is been folded into the foods sector in the latest version of E-DRAM. 4 The survey is conducted in years ending in 2 and 7 and data is released after processing. E-DRAM uses data from the 1997 release, which contains processed 1992 survey data.

Households' supply of labor, as a function of the wage rate, is called the “labor-supply function.” A more detailed description of the supply of labor is given in Chapter VII of the DRAM Report. Households' demand for goods or services, as a function of prices, is simply called the “demand function.” A more detailed description of the demand for goods and services is given in Chapter III of the DRAM Report, as well as in “Estimation of Household Demand for Goods and Services in California’s Dynamic Revenue Analysis Model,” (Berck, Hess, and Smith, Sept. 1997) currently available at www.are.berkeley.edu/~phess/demand.pdf.
The latter explains how the distribution of household spending across the 29 industrial sectors via the nine consumer goods sectors is based on analysis of U.S. Bureau of Labor Statistics' Consumer Expenditure Survey data.
So far, two types of agents have been described: firms and households. It remains to be explained how these agents relate. They relate through two types of markets: factor markets and goods-and-services markets. Firms sell goods and services to households on the goods-and-services markets. Households sell labor and capital services to firms on the factor markets. There is a price in each of these markets. There is a price for the output of each of the 29 industrial sectors. There is a price for labor, called the “wage,” and a price for capital services, called the “rental rate.” Equilibrium in a market means that the quantity supplied (which is a function of price) is equal to the quantity demanded (which is also a function of price) in that market. Equilibrium in the factor markets for labor and capital and in the goods-and services markets for goods and services defines a simple general equilibrium system. That is, there are 31 prices (the wage, the rental rate, and one for each of the 29 goods made by the 29 sectors) and these 31 prices have the property that they equate quantities supplied and demanded in all 31 markets. They are market-clearing prices. These relationships are shown in more detail in the figure below, called a “circular-flow diagram.” The outer set of flows, shown as solid lines, are the flows of “real” items, goods, services, labor, and capital. The inner flows, shown as broken lines, are monetary flows. Thus, firms supply goods and services to the goods-and-services market in return for revenues that they receive from the goods-and-services markets. Firms demand capital and labor from the factor markets and in return pay wages and rents to the factor markets. Households, the other type of agent in a simple model, buy, or in economic parlance, demand, goods and services from the goods-and-services markets and give up their expenditure as compensation. They sell capital and labor services on the factor markets and receive income in exchange. Demand Goods & Services Expenditure Revenue Supply Households Firms Income Rents Supply Demand Factors

The economy of California is far more complex than that shown in the figure above. There are not only final goods-and-services markets but also intermediate goods markets in which firms sell to firms. A typical example of this would be chemicals sold to agricultural firms. The final output of the chemical industry (perhaps fertilizer) is said to be an intermediate good in the agricultural industry. This type of market is demonstrated in the figure below. Here, part of the supply of a firm (chemical industry in the example) is not sold to households but rather to another firm in exchange for revenue. From the other firm’s point of view, it buys an input to production from a firm rather than from a household. The expense of buying the input is a cost of production. Chapter IV of the DRAM Report contains the model specification for these types of transactions, which are based upon a national input-output (I-O) table. Goods & Services House holds Supply Revenue Firms Costs Demand Intermediates Factors
California is an open economy, which means that it trades goods, services, labor, and capital readily with neighboring states and countries. In this model, all agents outside California are modeled in one group called “Rest of World (ROW).” No distinction is made between the rest of the U.S. and foreign countries. California interacts with two types of agents: foreign consumers and foreign producers. Taking the producers first, the figure below shows that the producers sell goods on the (final) goods-and-services markets and on the intermediate markets, i.e., they sell goods to both households and firms. The model takes these goods as being imperfect substitutes for the goods made in California. Agricultural products from outside of California (e.g., feed grains, bananas) are taken as being close to, but not identical to, California-grown products (e.g., avocados, fresh chicken). The degree to which foreign and domestic goods substitute for each other is very important, and the evidence is described in Chapter V. Foreign households buy California goods and services on the goods-and-services markets. They and foreign firms both can supply capital and labor to the California economy, and domestic migration patterns are described in Chapter VIII of the DRAM Report.

Demand (Exports) Goods & Services Capital Inflow Supply (Imports) Capital Inflow Supply (Imports) Capital Outflow Foreign House Holds House Holds Firms Inter mediates Foreign Firms Capital Inflow Capital Outflow Demand (Exports) Supply Capital Inflow Factors Demand
Finally, government is considered. Combining the taxing and spending effects of the three levels of government (federal, state, and local) gives the additional flows in the figure below. Beginning at the top, the figure shows that government buys goods and services and gives up expenditure. It supplies goods and services for which it may or may not receive revenue. Government also supplies factors of production, such as roads and education. The model does not currently include goods such as K-12 education as such goods are not always traded in organized markets. Government also makes transfers to households, which are not shown in the diagram. The middle section of the diagram shows the myriad of ways in which government raises revenue through taxation. Chapter II of the DRAM Report includes a detailed description of the government activities in the model.

Expenditure Demand Supply Revenue Import Duties Foreign Households House holds Goods & Services Income Taxes Property Taxes Social Insurance Factors Firms Sales Taxes Inter mediates Foreign Firms Corporate Income Taxes Non-Resident Income Tax Fees Licenses Rents Supply Demand Rents
The first step in constructing a CGE model is to organize the data. The traditional approach to data organization for a CGE model is to construct a Social Accounting Matrix (SAM). A SAM is a square matrix consisting of a row and column for each sector of the economy. Each entry in the matrix identifies an exchange of goods and services purchased by one sector from another sector (or itself). The entries along a row in the SAM show each payment received by that particular row sector from each column sector. Summing across the row gives total payments made to that row sector by all column sectors. The entries down a column in the SAM show the expenditures made by that particular column sector to all row sectors. Summing down a column gives total expenditures by that column sector to all row sectors. For accounting purposes, a SAM must "balance," i.e., the each row sum and corresponding column sum must be equal. This balancing ensures that no money "leaks" out of the economy, i.e. that all money received by firms (row sum) is spent by them (column sum).
There have been hundreds of CGE models built and used for analyzing public policy at the national and international level. Regional, or sub-national, CGE models are very similar in design to national and international models, but exhibit major differences in several key assumptions. The seven most important differences between national and regional CGE models are discussed below. The first, and maybe most important, difference is that regional CGE models do not require that regional savings equal regional investment. When Californians save more than California investors want to use, excess savings flow out of the state. When the converse is true, savings flow into the state. Rational economic agents would not accept less interest on their savings from California investors if higher interest rates were available in other states or countries. Conversely, rational investors in California would not pay higher interest for the use of Californian savings if other states or countries offered lower rates.

The second difference is that regional economies trade a larger share of their output. Therefore, trade is more important in regional models. Note that interstate trade is part of the Rest of World for California but ignored in national considerations of trade. The third difference is that regional economies face larger and more volatile migration flows than nations. Regional and international migration to California is a major factor in the State’s economy. The fourth difference between national and regional CGE’s is that regional economies have no control over monetary policy. The Federal Reserve is responsible for monetary policy and is a national institution. The fifth difference is that in regional models taxes are interdependent through deductibility. Some local taxes are deductible from incomes subject to California Personal Income Taxes and Bank and Corporation taxes. Some local and state taxes are deductible from incomes subject to Federal Personal Income Tax and may be eligible for deduction from corporate incomes for federal purposes. In E-DRAM, the personal tax deductibility is explicitly modeled. Since corporate deductibility is more uncertain and since the apportionment rules may reduce the connection to federal corporate taxes, corporate deductibility has not been included in E-DRAM. Sixth, while good data for a CGE are hard to find at the national level, in many cases they are nonexistent for regional economies. The E-DRAM uses published economic and statistical literature to simulate much of the data important to our model. In some cases, such as labor supply, a wide variety of results are presented in the literature. This problem is addressed in three ways: (1) values are chosen so as to avoid the extremes, (2) the model is tested to determine the degree to which results are dependent upon our assumptions (this process is called "sensitivity analysis"), and (3) the use of published literature, especially of national results, has been minimized. Seventh, the California CGE differs from a national CGE in that California faces a balanced-budget requirement. Even if this is ignored in the short run, bond markets tend to reflect this fact. When California issued bonds to cover short-term deficit spending in the early 1990s, bond ratings forced up the cost of borrowing. Ultimately, California would face unreasonable borrowing costs should it decide to maintain this level of borrowing.
The CGE models are not forecasting models; they are calibrated to reproduce a base year. In the case of E-DRAM, the model is constructed to exactly reproduce the economic conditions of fiscal year 1998/99. Of course, there are forecasting models. However, such models typically do not have the level of detail needed to examine dynamic policy effects. Given the paucity of California-specific data, it seems a better compromise to use a forecasting model, such as the one maintained by DOF, to set a base case and then use a policy model, such as DRAM, to analyze deviations from that case. The E-DRAM model incorporates two assumptions that require some comment. It assumes competitive behavior in all private sectors. This is a good first approximation, particularly at the level of a sector. The alternative, oligopoly behavior, may well be present, but the degree of markup of price over marginal cost is not likely to be significant. The second assumption is that involuntary unemployment is constant. This is unlikely to be strictly true. The model has voluntary unemployment, which are agents deciding to work less when the wage is lower. This assumption is common to all equilibrium models. Technical issues of model closure are described in Chapter IX of the DRAM Report. Once the major agents in the economy have been identified and the relationship between these agents has been specified, the model can be built. In E-DRAM, the algebraic representation of the relationships between the agents in the California economy is achieved with General Algebraic Modeling System

(GAMS). The model currently has 1,100+ equations, exclusive of definitions and of the code to read in and organize the data. All of the model’s equations and GAMS code are detailed in Chapter X.
Fuller description of common features shared by E-DRAM and DRAM is available in the report cited above (see footnote 5). The primary contents of that report, the presentation of which mirrors the sequence of tasks involved in building DRAM, are as follows. In Chapter II of the DRAM Report, the major agents in the economy are identified and aggregated into sectors. These aggregates are constructed to focus the model on the major industries, taxpayers, and government agencies in the California economy. Data sources are also identified. Chapters III through VIII of the DRAM Report review the literatures, functional forms, and elasticities relevant to the six primary behavioral equations that link all the various sectors of the model and drive its results. Chapter III of the DRAM Report reviews the literature on the economic behavior of households with respect to consumption and savings decisions. The literature on the production decisions of firms is examined in Chapter IV of the DRAM Report. Chapter V of the DRAM Report summarizes the literature on international and interregional trade. Investment theory is discussed in Chapter VI of the DRAM Report. Chapter VII of the DRAM Report covers the literature on regional labor-supply response to taxation and economic growth, while the literature on migration and economic growth is examined in Chapter VIII of the DRAM Report. After establishing the sectoring scheme, data sources, and behavioral equations for the model, all that remains before the actual model can be built is a description of the model-closure rules. Closure rules concern the mathematics of insuring that a solution exists to the 1,100+ equations of the model. Model closure is developed in Chapter IX of the DRAM Report. Chapter X of the DRAM Report describes the mathematical and corresponding GAMS notation for each equation in DRAM. It is a technical description of the complete California Dynamic Revenue Analysis Model.
5 , 6 Chapter XI of the DRAM Report presents some preliminary sensitivity analyses. Appendices follow Chapter XI of the DRAM Report. They include the original literature search by Dr. Berck and Mr. Dabalen in the Summer of 1995, explanations of notational methods used, lists of parameter and variable names used in the mathematical and software input files, and printed copies of the input files themselves.
For examining petroleum dependency issues in particular, the E-DRAM built for ARB as described in Berck and Hess (Feb. 2000) is enhanced in three ways. First, Petroleum sector data is modified. Second, the 1998/1999 base year model is extrapolated out to 2020 and 2050 based on state population, personal income, and industry-specific forecasts. Third, parameters to adjust for technological change in the form of increased fuel efficiency and fuel displacement are incorporated into the model. Each of these enhancements is discussed in turn in the subsections below. 5 See Berck, Hess, and Smith (Sept. '97) for revisions to the consumer demand portion of the model. 6 Modification of equations from DRAM to E-DRAM are discussed in “Developing a Methodology for Assessing the Economic Impacts of Large Scale Environmental Regulations,” (Berck and Hess, Feb. 2000). Changes introduce parameters that facilitate running policy scenarios as some combination of price, intermediate good, and/or investment changes.

As indicated in Section 6.1.1.1, E-DRAM's original industrial accounts are national accounts scaled to the state level using California employment data. These accounts have been reconciled with more California specific Petroleum sector figures provided by ADL in consultation with ARB, the California Energy Commission (CEC), and the Berkeley team. ADL estimated California refinery flows from EIA data.
7 A summary of these data for 1999 is shown in Table 6-1. Several assumptions were made to get both specific California data and data for California supplies to Nevada and Arizona. First, ADL assumed that California refining capacity and products are 72% of PAD V (28% is associated primarily with refining in Washington). Second, we also assumed that California refineries supply 80% of Nevada’s needs and 50% of Arizona’s needs. Prices for products indicated in Table 6-1 are actual 1999 prices as reported by EIA. For example, average crude oil price was $17.81/bbl in 1999 and average finished motor gasoline price was $1.30/gal. Estimates for 2020 and 2050 were obtained by first determining the overall demand for finish products. This was estimated from the CEC projections of baseline fuel demands (CEC, 2001). In this report, fuels are projected to grow at the following annual rates: Product % Growth rate/yr Gasoline Diesel Residual Jet 1.6 2.4 2.0 3.4 We also assumed a nominal growth rate of 1% per year for LPG and other products. The California growth rates were also applied to Nevada and Arizona. Based on the total products supplied in 2020 and 2050, we then estimated how the refineries would produce these fuels. Several assumptions were used to make these predictions. California refinery capacity was assumed to grow at 0.5% per year through 2020 (Stillwater). This adds about 11% to the 1999 capacity of about 628.8 million barrels. After 2020 the capacity was held fixed and increase demand had to be met with importing refined products. 7 Energy Information Administration, Office of Oils & Gas, U.S. DOE, “Petroleum Supply Annual 1999,” Vol. 1, June 2000 ( www.eia.doe.gov/oil_gas/petroleum/data_publications/petroleum supply_annual/psa_volume 1/psa_volume1.html)

Description 1999 2020 2050 000 bbls $ million 000 bbls $ million 000 bbls $ million Imports to California
Crude oil Natural gas liquids Other liquids (unfinished oils and gasoline blend components like oxgenates) 391,395 (1) 29,227 1,228 6,971 608,140 13,683 — — — 37,979 1,595 698,236 — 15,710 — 75,959 3,190 Refined products Total Import Value
California Oil Production
64,514 273,019 2,723 10,921 4,862 291,000 90,096 15,725 31,003 2,027 1,192,500 64,438 — 83,339 — Total Input to California
California Transportation Consumption
Finished motor gasoline Distilled fuel oil Residual fuel oil Jet fuel Liquefied petroleum gases Other 15,784 33,030 335,633 18,364 463,151 31,902 64,078 27,881 98,673 384 3,796 3,199 128,190 8,884 317 68,642 987 2,383 218,894 15 148 592 5,236 6,680 30 258 745,648 261,128 124,336 592 5,236 83,339 51,360 18,096 1,788 596,829 18,213 30 258 California demand
California Other Consumption
Finished motor gasoline Distillate fuel oil Residual fuel oil Jet fuel Liquefied petroleum gases Other Crude oil California demand
Exports from California
530,445 24,427 884,705 48,740 2,158 118 2,697 186 10,584 684 — 11,787 62,101 328 12 — 374 3,391 16,421 1,404 — 14,630 85,146 1,138 30 — 586 5,873 87,314 35,610 4,222 120,300 7,813 634 — — 1,733,769 89,745 4,342 33,451 2,544 — 14,630 85,146 140,114 — 299 2,318 54 — 566 5,873 9,120 —

Refined products California production to Arizona, Nevada for transportation and other Finished motor gasoline Distillate fuel oil Residual fuel oil Jet fuel Liquefied petroleum gases Other Out of State Demand Export value Total output 62,425 44,908 18,054 114 11,497 3,179 7,268 2,439 2,457 901 127 284 69,292 61,932 34,968 1 278 280 25,504 3,976 9,969 3,420 4,266 2,423 4 778 201 492 85,019 4,048 7,121 33,987 136,628 8,164 11,584 63,744 69,292 3,420 99,707 71,231 506 69,538 6,868 4,936 7 2,122 3,976 9,969 201 492 254,928 14,626 18,046 111,211 California oil production was assumed to decline from 1999 levels of 273 million barrels to 90 million barrels in 2020 and no in-state production in 2050. This estimate was based on linear extrapolation of either historical production or reserves. Either of these indicated California production being eliminated in the 2030-2040 time frame. Also, Alaska production (assuming no new drilling) declines to zero in the 2020-2030 time frame. Thus, California will be far more dependent on foreign oil supplies in the post 2020 years. 2020 and 2050 prices were also determined or scaled from CEC projections. CEC projects crude oil prices at $22.50/bbl and gasoline at $1.64/gallon and diesel at $1.65 gallon. So the prices in Table 6-1 are comparable for 2020 and 2050 and are higher than 1999 by about the ratio of $22.50 to $17.81. There are several interesting trends suggested in the data shown in Table 6-1. California will be importing more crude in the out years due to dwindling in-state production. In 1999, crude imports (including mostly Alaska) were 391 million barrels. This will grow to 698 million barrels in 2050 and this supply will all have to come from foreign sources. In 1999, California imported 64.5 million barrels of refined products, which will grow to 1.19 trillion barrels in 2050. Table 6-2 itemizes our estimate of California refinery supply and demand expressed in dollars. This also shows in the out years that California will be much more dependent on imported refined products.
Description
Supply California refineries Refined products imported Total demand
1999 $ million
32,413 2,723 35,136
2020 $ million
52,413 15,725 68,137
2050 $ million
52,483 64,438 116,922

California Export to Arizona, Nevada Export from refineries Crude imports Supply Demand Import of refined products 28,649 4,048 2,439 6,971 32,413 35,136 (2,723) 56,553 8,164 3,420 13,683 52,413 68,137 (15,725) 98,876 14,626 3,420 15,710 52,483 116,922 (64,438) Modifications to the petroleum sector 1999 base data are as follows. First, E-DRAM's original petroleum sector (PETRO) import and export figures were replaced with those provided by ADL.
8 Petroleum exports from California, as recorded in the (PETRO, ROW) cell of the SAM, were decreased from $11 billion down to $6.5 billion.
9 Petroleum imports to California, as recorded in the (ROW, PETRO) cell of SAM, were increased from $0.5 billion up to $2.7 billion. Second, E-DRAM's California petroleum demand was raised to match ADL’s California petroleum demand estimate by increasing in-state consumer demand for petroleum (CFUEL).
10 Operationally, this was achieved by increasing the SAM cell (PETRO, CFUEL) from $6.3 billion to $13.7 billion. For consistency, this change was traced through household (HOUSE#) spending on CFUEL by raising each SAM (CFUEL, HOUSE#) cell by 20%. Increased fuel spending was offset via 0.8-1.6% (depending on each household sectors' overall expenditure levels) spending cuts applied uniformly across the other eight consumer good sectors. Third, E-DRAM's petroleum supply was raised to equal California demand ($28.6 billion) plus exports from California ($6.5 billion) minus imports to California ($2.7 billion) as calculated from the revised numbers above. This supply shift was implemented by increasing petroleum sector inputs (intermediates, factors, and taxes thereon) by 2.2% across the board.
11 Once these changes were made, the 1999 SAM had to be re-balanced, that is the SAM needed to be adjusted so that the row and column totals were again the same. Re-balancing was done using a program written by Sherman Robinson and Moataz El-Said in November 2000.
12 8 Trade flow data is typically one of the weakest links in regional economic models. 9 Following convention, matrix cells are referenced by (row name, column name). 10 All adjustment came through the consumer sector due to perceived weakness in E-DRAM's household demand data vise-a-vise government and industry demand data and the relative strength of indications from outside sources that household consumption was higher than the model's original base data suggested. 11 Production is constant returns-to-scale. 12 The method is described in S. Robinson, A. Cattaneo and, M. El Said, "Updating and Estimating a Social Accounting Matrix Using Cross Entropy Methods." TMD Discussion Paper No. 58, IFPRI, August 2000. (This paper was also to be published in Economic Systems Research, March/June 2001.)

As discussed in Section 6.1.3, E-DRAM is not a forecasting model, but rather a model constructed to exactly reproduce the economic conditions of fiscal year 1998/99. To answer questions concerning the impacts of petroleum dependency reduction strategies far into the future, E-DRAM must be augmented to reflect future conditions. To "re-base" E-DRAM, i.e., move from a model of the 1999 economy to models of the economy in 2020 and 2050, E-DRAM's input data must be modified to reflect economic conditions in those "out years." The following process leaves the basic structure of economic relationships intact, while scaling up 1998/1999 monetary and employment data using state personal income, population, and industry-specific forecasts.
The first step in re-basing E-DRAM is to forecast economic growth. Borrowing from the University of California, Los Angeles (UCLA) business forecast, an average annual growth rate of 2.84% was assumed for the years 2000 to 2020; an average annual growth rate of 2.58% was assumed for 2020 to 2050. Compounding these growth rates delivered scale factors for re-basing monetary flows recorded in the SAM. In re-basing from 1998/1999 to 2020, each 1999 SAM transaction – unless otherwise noted below – was scale up by a factor of 2.2515; in re-basing from 2020 to 2050, each 2020 SAM transaction – unless otherwise noted below – was scaled by a factor of 2.1520.
13 The second, related, step in re-basing E-DRAM is to forecast population and/or employment growth. DOF projections suggest a California population growth rate of 1.36% annually. Compounding this rate delivered scale factors for re-basing employment data. In re-basing from 1998/1999 to 2020, each employment-by-industry cell in the 1999 MSC matrix (in the MSC input file) was scaled up by a factor of roughly 1.3; in re-basing from 2020 to 2050, the each employment-by-industry cell in the 2020 MSC matrix was scaled up by roughly 1.5.
14 The third step in re-basing E-DRAM is to reconcile income and property tax accounts. Receipts scaled up via step one above, change model calculated rates – which act as incentives in economic decision making – when the population grows at a different pace than the economy as a whole. Rates and receipts are reconciled via tax adjustment parameter, TAXCVC (GI,H).
15
Petroleum sector and energy and mining sector (ENMIM), supply, demand, and trade flows were scaled according to ADL’s projections as detailed in Tables 6-1 and 6-2.
16 13 The UCLA forecast for state personal income (SPI) is $1.1 trillion in 2000 and implies an average annual SPI growth rate of 2.84% to 2012. Given that the 2000 SPI forecast is roughly 28% above the original 1998/1999 E DRAM SPI level, and extrapolating the 2.84% growth rate out to 2020, each cell of the SAM – unless otherwise noted – was scaled up by 1.28*(1.0284) 20 = 2.2515 in re-basing the model from '98/'99 to 2020. Assuming 2.58% average annual SPI growth from 2020 to 2050 led to scaling each cell of the SAM – unless otherwise notes – by a by factor of (1.0258) 30 = 2.1520. 14 Scale factors for employment in the petroleum sector and the energy and mining sector were slightly lower, in accordance with growth forecasts for those industries (see next section). 15 GI indexes government income tax units, i.e., federal and state income tax as well as local property tax; H indexes household types (which, recall, are classified by income tax bracket). 16 Capital stocks in the energy sectors were fixed to reflect capacity constraints.

ADL projects demand (including exports) for California refined petroleum rising from $35.1 billion in 1999 to $68.1 billion in 2020, and California supply (excluding imports) rising from $32.4 billion to $52.4 billion over the same time period. Operationally, this meant increasing California refined petroleum demand (all cells, except ROW, in the PETRO row of SAM) by a factor of nearly 2, while increasing California refined petroleum supply (all cells, except ROW, in the PETRO column of SAM) by a factor of roughly 1.6 when re-basing E-DRAM from 1998/1999 to 2020.
17 The gap in domestic supply and demand was offset by higher net imports. Refined imports, SAM cell (ROW, PETRO), were raised from $2.7 billion in the 1999 SAM to $15.7 billion in the 2020 SAM; refined exports, SAM cell (PETRO, ROW) were raised from $6.5 billion to $11.6 billion. ADL projects California crude oil production dropping from roughly $4.9 billion in 1999 to roughly $2 billion in 2020. With crude oil accounting for 79% of energy and mining sector (ENMIN) output value in 1999, that sector's production was projected to be only 2.4% higher in 2020 than 1999.
18 Assuming ENMIN sector demand (including exports) grows at 2.84% annually along with rest of the economy, the resulting gap in domestic supply and demand was offset with higher imports. ENMIN sector imports, SAM cell (ROW, ENMIN), were raised from $17.5 billion in the 1999 SAM to $36.0 billion in the 2020 SAM. Once these changes were made, the 2020 SAM was re-balanced using the cross entropy program written by Sherman Robinson and Moataz El-Said.
Gaps between supply and demand are more pronounced in the 2050 projections. ADL projects demand (including exports) for California refined petroleum rising from $68.1 billion in 2020 to $116.9 billion in 2050, and California supply (excluding imports) rising from $52.4 billion to only $52.5 billion over the same time period. Operationally, this meant increasing California refined petroleum demand (all cells, except ROW, in the PETRO row of SAM) by a factor of 1.775, while increasing California refined petroleum supply (all cells, except ROW, in the PETRO column of SAM) by a factor of roughly 1.008 when re-basing E-DRAM from 2020 to 2050.
19 The gap in domestic supply and demand was offset by higher net imports. Refined imports, SAM cell (ROW, PETRO), were raised from $15.7 billion in the 2020 SAM to $64.4 billion in the 2050 SAM; refined exports, SAM cell (PETRO, ROW) were raised from $11.6 billion to $18.0 billion. ADL projects California crude oil production dropping from roughly $2.0 billion in 2020 to zero in 2050. With crude oil accounting for 31% of energy and mining sector (ENMIN) output value in 2020, that sector's production was projected to be 19% higher in 2050 than 2020.
20 Assuming ENMIN sector demand (including exports) grows at 2.58% annually along with rest of the economy, the resulting gap in 17 The increases in consumer petroleum demand, SAM cell (PETRO, CFUEL), was again translated through household sectors' increased expenditure on CFUEL and decreased expenditure on other consumer goods as discussed in Section 6.2.1. 18 The remaining 21% of the ENMIN sector was assumed to grow at the same rate as the rest of the economy, i.e., 2.84% annually. 19 The increases in consumer petroleum demand, SAM cell (PETRO, CFUEL), was again translated through household sectors' increased expenditure on CFUEL and decreased expenditure on other consumer goods as discussed in Section 6.2.1. 20 The remaining 69% of the ENMIN sector was assumed to grow at the same rate as the rest of the economy, i.e., 2.58% annually.

domestic supply and demand was offset with higher imports. ENMIN sector imports, SAM cell (ROW, ENMIN), were raised from $36.0 billion in the 1999 SAM to $57.0 billion in the 2020 SAM. Once these changes were made, the 2050 SAM was re-balanced using the cross entropy program written by Sherman Robinson and Moataz El-Said.
Parameters for modeling technological change built into the original E-DRAM were augmented for the current analyses. As described in Berck and Hess (Feb. 2000), the original E-DRAM allows for changes in production technology. Each industrial sector in E-DRAM is implicitly characterized by a production function that relates output to factor (capital and labor) and intermediate inputs. Technological change is modeled by altering the relationships of input mix per unit of output as follows. Industry J’s demand for intermediates from industry I per unity of output is governed by production parameters AD(I,J), which are input-output coefficients calculated from primary data contained in the SAM. These coefficients can be altered via technology multiplier parameters REG1(I,J). Changing REG1(I, 'industry J label' ) from its default setting of unity to 0.9, for example, simulates a technological change enabling one unit of industrial good J to be produced using only 90% of the intermediate inputs (from all 29 industries) previously required. Specifying AD(‘industry I label’, ‘industry J label’) = 0.9, in contrast, simulates a technological change enabling one unit of good J to be produced using 90% of the intermediate inputs previously required from industry I (with inputs from the 28 other industries unchanged). See Section 6.4 for implementation. For the current project, an additional parameter is added to allow for technological changes in consumption. This new parameter is REG16(I,C), where C indexes the nine consumer good categories. REG16(I,C) is inserted into E-DRAM as a technology multiplier parameter wherever parameter PHI(I,C) appears.
21 PHI(I,C) regulates the distribution of household spending on industry I via consumer good C. Changing REG16(I, 'consumer good C label') from its default setting of unity to 0.8, for example, simulates a technological change enabling one unit of consumer good C to be enjoyed using only 80% of the inputs previously required (from all 29 industries). Specifying REG16('industry I label', 'consumer good C label') in contrast, simulates a technological change enabling one unit of consumer good C to be enjoyed using 80% of the inputs previously required from industry I (with inputs from the other 28 industries unchanged). See Section 6.4 for implementation.
The table below displays selected input data and corresponding model output for the 1999, 2020, and 2050 base case models. Comparing the columns labeled "DATA" and "MODEL" for any given year indicates that the model is well calibrated, i.e., it produces model solutions that match the input data to within tenths or hundredths of one percent. Achieving such calibration is an essential starting point for policy analysis, as policy scenario results that differ from the base model by less than the level of calibration are not empirically significant. Comparing across model years demonstrates how the economy grows by roughly the scale factors discussed in Section 6.2.2.1. State output and personal income increase by factors of roughly 2.25 from 1999 to 2020 and 2.15 from 2020 to 2050 respectively, while state population and employment grow by factors of roughly 1.3 from 1999 to 2020 and 1.5 from 2020 to 2050. The petroleum (PETRO) and energy and mining (ENMIN) sectors both also grow by roughly the scale factors implemented.
22 21 PHI(I,C) appears in equations 1.05 and 1.06. 22 Small divergence between scaling input to the model and output from the model occur due to SAM balancing.

CA OUTPUT ($BILLION) % CHANGE CA OUTPUT CA PERSONAL INCOME ($BILLION) % CHANGE CA PERS. INC.
CONSUMER PRICE INDEX (BASE=1) % CHANGE AGGREGATE CPI POPULATION (MILLION FAMILIES) % CHANGE POPULATION WAGE INDEX (BASE = 100) % CHANGE WAGE INDEX LABOR DEMAND (MILLIONS) % CHNGE LABOR DEMAND RETURN TO K INDEX (BASE=100) % CHANGE RETURN TO K INDEX CAPITAL STOCK ($100 BILLION) % CHANGE CAPITAL STOCK ENMIN OUTPUT ($BILLION) % CHANGE OUTPUT JOBS (MILLIONS) % CHANGE JOBS PRICE (BASE=1) % CHANGE PRICE IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS PETRO OUTPUT ($BILLION) % CHANGE OUTPUT JOBS (MILLIONS) % CHANGE JOBS PRICE (BASE=1) % CHANGE PRICE IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS 1999 DATA 1377.0067
BASE MODEL 1378.0905
0.08% 891.6942
1.0000
892.4894
0.09% 1.0000
23.1413
100.0000
14.0459
100.0000
14.5720
0.00% 23.1431
0.01% 100.0517
0.05% 14.0483
0.02% 100.0060
0.01% 14.5863
0.10% 2020 DATA 3075.0665
BASE MODEL 3078.0223
0.10% 2007.3821
1.0000
2009.5373
0.11% 1.0000
30.7317
100.0000
18.6552
100.0000
32.7161
0.00% 30.7362
0.01% 100.0688
0.07% 18.6605
0.03% 100.0067
0.01% 32.7557
0.12% 2050 DATA 6561.4202
BASE MODEL 6568.5732
0.11% 4319.8863
1.0000
4325.2331
0.12% 1.0001
46.0883
100.0000
27.9572
100.0000
70.3030
0.01% 46.0978
0.02% 100.0880
0.09% 27.9673
0.04% 100.0075
0.01% 70.4023
0.14% 5.8738
0.0178
1.0000
17.5309
0.4377
24.8013
0.0220
1.0000
2.8054
6.4755
5.8789
0.09% 0.0178
0.16% 1.0001
0.01% 17.5404
0.05% 0.4375
-0.06% 24.8156
0.06% 0.0220
0.09% 1.0001
0.01% 2.8058
0.01% 6.4746
-0.01% 6.2035
0.0182
1.0000
35.9865
1.0973
39.2783
0.0292
1.0000
15.6811
11.9998
6.2086
0.08% 0.0182
0.15% 1.0001
0.01% 36.0105
0.07% 1.0965
-0.07% 39.3048
0.07% 0.0292
0.10% 1.0001
0.01% 15.6834
0.01% 11.9979
-0.02% 7.6830
0.0216
1.0000
57.3622
2.6420
39.2124
0.0294
1.0000
63.6238
19.1462
7.6887
0.07% 0.0216
0.14% 1.0000
0.00% 57.4093
0.08% 2.6396
-0.09% 39.2540
0.11% 0.0295
0.15% 1.0000
0.00% 63.6368
0.02% 19.1419
-0.02%
The subsections below analyze four alternate strategies for reducing California's petroleum dependence. Each scenario is built around two elements: (1) reduced gasoline demand from improved light-duty

vehicle fuel economy, and (2) diesel fuel displacement from gas-to-liquid (GTL) or Fischer Tropsch diesel fuels. The scenarios were constructed to try to “bound” the possible impacts to the California economy. Scenario 1 combines off-the-shelf fuel efficiency improvements in light-duty vehicles with a 33 percent blend of FTD in diesel fuel to meet ARB’s future ULSD specification. Conversely, Scenarios 3 and 4 incorporate more aggressive and therefore more costly fuel efficiency or displacement options. These strategies, developed in a collaborative process between ARB, CEC, and ADL are summarized in Appendix C. Each strategy is described briefly and GAMS code for its implementation into E-DRAM presented. Select model output is given and discussed. Each scenario is modeled and coded as some combination of increased transportation costs and altered – generally decreased – fuel costs. The rationale is that more efficient transportation is costlier to produce, but saves fuel. Industries and households buy transportation and fuel. In E-DRAM, industries buy some vehicle engines directly, while households buy them indirectly via the consumer goods sectors. Industrial purchases from the engine (ENGIN) and petroleum (PETRO) sectors are recorded in SAM cells ('ENGIN', I) and ('PETRO', I) respectively. Households' purchases from the consumer transportation sector (CTRANS) are recorded in the SAM cells (I, 'CTRANS'). Households' purchases of petroleum via the consumer fuel sector (CFUEL) are recorded in SAM cells (I, 'CFUEL'). Following the explanation of technological change parameters in Section 6.2.3, increases in consumer and industrial transportation costs are modeled using parameters REG16(I, 'CTRNS') and REG1('ENGIN',I) respectively. Decreases in consumer and industrial fuel costs are modeled using parameters REG16('PETRO', 'CFUEL') and REG1('PETRO', I) respectively. Switches from petroleum to hydrogen based fuels (scenario 3 only) are modeled as increases in REG1('ENMIN', 'PETRO'), accompanied by offsetting increases in REG1('CHEMS', 'PETRO').
23 The CEC estimates that residential use accounts for roughly 90% of gasoline consumption in the state. Hence, 90% of projected increases in engine costs are apportioned to household and 10% are apportioned to industries. Likewise, 90% of projected fuel savings are apportioned to households and 10% are apportioned to industries. The following four subsections detail four alternate policy scenarios for reducing California's petroleum dependence. A short policy description, GAMS code that models the projected costs and benefits via the channels outlined immediately above, and select E-DRAM output along with corresponding analysis are presented for each.
24 Scenario 1 is a combination of fuel efficiency measures applied to light-duty vehicles starting in 2008 and FTD blended with other diesel feedstocks at 33% to meet ARB’s future ULSD specification. Table 6-5 summarizes the costs and benefits of this combined strategy. Light-duty vehicle costs in 2020 and 2050 where taken from CALCARS analyses performed by CEC. The EEA/Duleep case phases in off-the-shelf fuel economy improvements in the early years of implementation and introduces higher fuel economy technologies in the later years. The benefits at the household level result from fuel savings associated with the higher fuel economy technologies. The estimates for 2020 and 2050 include vehicles that have been introduced earlier; that is the technology is applied to new vehicles starting in 2008 and continuous as other vehicles retire from the fleet. Thus, the cost and benefits are a “slice” in time of what the fleet 23 This implementation assumes that the much the same fuel distribution system would be used regardless of the fuel variety. 24 Numbers in the illustrative scenario coding correspond to 2020 cost/benefit projections.

would look like and what the costs would be. These costs were then input into the model to assess economic impact.
Changes in Consumer Expenditures Million 2002 $ 2020 2050 Changes in Sector Revenue
Cost Household (inc. vehicle cost) Household (inc. PZEV cost) Commercial (inc. GTL-diesel cost) Total Cost 1,460 501 125 4,900 812 146 2,087 5,858 Benefit Vehicle Mfg. (inc. vehicle revenue) Vehicle Mfg. (inc. PZEV revenue) Foreign GTL Producer (inc. revenue) Total Benefits Benefits Household (dec. gasoline expenditure 3,264 14,617 Cost Refiners (decrease in revenue) California Excise Tax (dec. revenue) Federal Excise Tax (dec. revenue) Total Benefits 3,264 14,617 Total Costs
Million 2002 $ 2020 2050
1,460 4,900 501 125 358 358 812 146 2,087 2,547 5,858 11,409 1,604 1,604 3,264 14,617 Scenario 1 is implemented in the following manner (see footnotes for actual GAMS code). The cost of consumer transportation (CTRNS) increases by 90% of projected consumer cost. These additional costs are inserted such that the new, higher amount of consumer transportation spending is expressed as the appropriate multiple of old spending.
2 5 The cost of industrial engines increases by 10% of the projected consumer cost, plus the commercial costs. These additional costs are inserted such that the new, higher amount of industrial spending on engines is expressed as the appropriate multiple of old spending.
2 7 90% of the projected savings from increased fuel efficiency accrue to consumers. These savings are inserted such that the new, lower amount of consumer fuel spending is expressed as the appropriate fraction of old spending.
2 8 25 REG16(I,'CTRNS') = (SUM(J, SAM(J,'CTRNS')) + 0.9*1.961)/SUM(J, SAM(J,'CTRNS')); 27 REG1('ENGIN',I) = (SUM(J,SAM('ENGIN',J)) + .1*1.961 + .125)/SUM(J,SAM('ENGIN',J));

29 The table below compares selected results for base model and Scenario 1 runs of E-DRAM in both 2020 and 2050. Results show that scenario 1 slightly reduces state output (by 0.10% in 2020 and 0.17% in 2050) while slightly increasing state personal income (by 0.1% in 2050). Real personal income (what's reported in the table) rises while output falls because of increased consumer purchasing power due to improved fuel efficiency. Results indicate that the price of consumer fuel – interpreted as the price of vehicle miles traveled – is roughly 3 % lower in 2020 and 7% lower in 2050 in Scenario 1 than in base. Increased fuel efficiency reduces the demand for refined petroleum products. E-DRAM predicts petroleum sector output being 4% lower in 2020 and 16% lower in 2050 under Scenario 1 vs. base. Decreased petroleum sector output adversely affects upstream crude oil suppliers. The model predicts energy and mining sector output being 4% lower in 2020 and 16% lower in 2050 under Scenario 1 than base. Money freed from fuel expenditure is spent in other sectors. Scenario 1 raises both food (FOODS) and apparel (APPAR) sector output by roughly 2% over base in 2020 and by 5% over base in 2050. Sectors such as motor vehicle manufacturing (MOTOR) that rely heavily on combustion engine inputs, see costs rise; thus their prices rise and output falls. The price of consumer transportation (CTRANS) rises 0.72% and 0.95% in 2020 and 2050 respectively, while motor vehicle sector output falls 0.35% and .50% in those same times. 28 REG16(I,'CFUEL') = (SUM(J, SAM(J,'CFUEL')) - .9*3.264 )/SUM(J, SAM(J,'CFUEL')); 29 REG1('PETRO',I) = (SUM(J,SAM('PETRO',J)) - .1*3.264)/SUM(J,SAM('PETRO',J));

CA OUTPUT ($BILLION) % CHANGE CA OUTPUT CA PERSONAL INCOME ($BILLION) % CHANGE CA PERS. INC.
LABOR DEMAND (MILLIONS) % CHNGE LABOR DEMAND PRICE OF CFOOD PRICE OF CHOME PRICE OF CFUEL PRICE OF CFURN PRICE OF CCLOTH PRICE OF CTRANS PRICE OF CMED PRICE OF CAMUS PRICE OF COTHR ENMIN OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS PETRO OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS ENGIN OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS CHEMS OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS FOODS OUTPUT ($BILLION) % CHANGE OUTPUT APPAR OUTPUT ($BILLION) % CHANGE OUTPUT MOTOR OUTPUT ($BILLION) % CHANGE OUTPUT 2020 BASE MODEL 3078.0223
SCNRIO1 3074.9243
0.10% 2009.5373
0.11% 18.6605
0.03% -0.10% 2009.5213
0.00% 18.6767
0.09% 1.0001
1.0000
1.0000
1.0001
1.0001
1.0000
1.0001
1.0000
1.0000
1.0001
1.0000
0.9687
1.0001
1.0001
1.0072
1.0002
1.0001
1.0000
2050 BASE MODEL 6568.5732
SCNRIO1 6557.2797
0.11% 4325.2331
0.12% 27.9673
0.04% -0.17% 4329.6794
0.10% 28.0326
0.23% 1.0001
1.0001
1.0000
1.0001
1.0001
1.0001
1.0001
1.0001
1.0001
1.0000
0.9999
0.9324
1.0000
1.0000
1.0095
1.0003
0.9999
0.9999
6.2086
0.08% 36.0105
0.07% 1.0965
-0.07% 39.3048
0.07% 15.6834
0.01% 11.9979
-0.02% 40.4675
0.05% 9.0494
0.02% 13.8359
-0.03% 30.2836
0.22% 39.3028
0.01% 2.0905
-0.01% 92.9579
0.14% 25.9513
0.20% 18.2243
0.23% 6.0575
-2.43% 34.8290
-3.28% 1.1122
1.43% 37.6902
-4.11% 15.5646
-0.76% 12.0739
0.63% 40.5818
0.28% 9.0815
0.35% 13.7822
-0.39% 30.6482
1.20% 39.2943
-0.02% 2.0910
0.02% 95.1127
2.32% 26.4969
2.10% 18.1613
-0.35% 7.6887
0.07% 57.4093
0.08% 2.6396
-0.09% 39.2540
0.11% 63.6368
0.02% 19.1419
-0.02% 87.0335
0.05% 19.4495
0.04% 29.7408
-0.05% 64.9941
0.24% 84.2137
0.02% 4.6502
-0.02% 200.2299
0.17% 55.8814
0.25% 39.3478
0.24% 7.2328
-5.93% 52.2725
-8.95% 2.7452
4.00% 32.6620
-16.79% 62.1426
-2.35% 19.5219
1.99% 87.2217
0.22% 19.5153
0.34% 29.6307
-0.37% 66.6697
2.58% 84.1483
-0.08% 4.6542
0.09% 210.4874
5.12% 58.7842
5.19% 39.1508
-0.50%
20

Scenario 2 is similar to Scenario 1 but incorporates more aggressive fuel economy technologies in light duty vehicles. In this case, technology costs and benefits were determined from ACEEE analysis for advanced fuel economy improvements. It was assumed that these improvements would be implemented in all new light-duty passenger cars and trucks starting in 2008. The ACEEE-Advance case is more aggressive in increasing fuel economy compared to the EEA/Duleep case and the ACEEE costs tend to be lower than those estimated by EEA. Further, the EEA technologies are phased in at a much slower penetration than those assumed in this scenario. Table 6-3 shows our estimates of the economic inputs for modeling. As indicated, costs are higher in 2020 compared to Scenario 1 primarily due to the high penetration rate. Likewise, benefits are also considerably higher in 2020. At 2050 the two scenarios are more similar since EEA has fully phased in the higher fuel economy technologies and the ACEEE technologies are also fully phased in. Scenario 2 also includes the GTL or FTD blend as in Scenario 1.
Million 2002 $ Million 2002 $ Changes in Consumer Expenditures 2020 2050 Changes in Sector Revenue 2020 2050
Cost Household (inc. vehicle cost) Household (inc. PZEV cost) Commercial (inc. GTL-diesel cost) 4,197 6,794 501 125 812 146 Benefit Vehicle Mfg. (inc. vehicle revenue) Vehicle Mfg. (inc. PZEV revenue) Foreign GTL Producer (inc. revenue) Total Cost Benefits Household (dec. gasoline expenditure 4,824 9,284 7,752 19,746 Total Benefits Cost Refiners (decrease in revenue) California Excise Tax (dec. revenue) Federal Excise Tax (dec. revenue) Total Benefits 9,284 19,746 Total Costs 4,197 6,794 501 125 812 146 4,824 7,246 7,752 15,411 1,019 1,019 2,167 2,167 9,284 19,746 Scenario 2 is implemented in the following manner.
21

The cost of consumer transportation (CTRNS) increases by 90% of projected consumer cost. These additional costs are inserted such that the new, higher amount of consumer transportation spending is expressed as the appropriate multiple of old spending.
3 0 The cost of industrial engines increases by 10% of the projected consumer cost, plus the commercial costs. These additional costs are inserted such that the new, higher amount of industrial spending on engines is expressed as the appropriate multiple of old spending.
3 1 90% of the projected savings from increased fuel efficiency accrue to consumers. These savings are inserted such that the new, lower amount of consumer fuel spending is expressed as the appropriate fraction of old spending.
3 2 10% of the projected savings from increased fuel efficiency accrue to industry. These savings are inserted such that the new, lower amount of industrial spending on fuel is expressed as the appropriate multiple of old spending.
33 The table below compares selected results for base model and Scenario 2 runs of E-DRAM in both 2020 and 2050. Results show that Scenario 2 slightly reduces state output (by 0.26% in 2020 and 0.23% in 2050) while leaving state personal income essentially unchanged. Real personal income remains constant while output falls because of increased consumer purchasing power due to improved fuel efficiency. Results indicate that the price of consumer fuel – interpreted as the price of vehicle miles traveled – is roughly 9% lower in both 2020 and 2050 in Scenario 2 than in base. Increased fuel efficiency reduces the demand for refined petroleum products. E-DRAM predicts petroleum sector output being 12% lower in 2020 and 23% lower in 2050 under Scenario 2 vs. base. Decreased petroleum sector output adversely affects upstream crude oil suppliers. The model predicts energy and mining sector output being 7% lower in 2020 and 8% lower in 2050 under scenario two than base. Money freed from fuel expenditure is spent in other sectors. Scenario two raises both food and apparel sector output by 6-7% over base in 2020 and 2050. Sectors such as motor vehicle manufacturing that rely heavily on combustion engine inputs, see costs rise; thus their prices rise and output falls. The price of consumer transportation rises 0.17% and 0.13% in 2020 and 2050 respectively, while motor vehicle sector output falls 0.81% and .68% in those same times. 30 REG16(I,'CTRNS') = (SUM(J, SAM(J,'CTRNS')) + .9*4.698)/SUM(J, SAM(J,'CTRNS')); 31 REG1('ENGIN',I) = (SUM(J,SAM('ENGIN',J)) + .1*4.698 + .125)/SUM(J,SAM('ENGIN',J)); 32 REG16(I,'CFUEL') = (SUM(J, SAM(J,'CFUEL')) - .9*9.284 )/SUM(J, SAM(J,'CFUEL')); 33 REG1('PETRO',I) = (SUM(J,SAM('PETRO',J)) - .1*9.284)/SUM(J,SAM('PETRO',J));
22

CA OUTPUT ($BILLION) % CHANGE CA OUTPUT CA PERSONAL INCOME ($BILLION) % CHANGE CA PERS. INC.
LABOR DEMAND (MILLIONS) % CHNGE LABOR DEMAND PRICE OF CFOOD PRICE OF CHOME PRICE OF CFUEL PRICE OF CFURN PRICE OF CCLOTH PRICE OF CTRANS PRICE OF CMED PRICE OF CAMUS PRICE OF COTHR ENMIN OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS PETRO OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS ENGIN OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS CHEMS OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS FOODS OUTPUT ($BILLION) % CHANGE OUTPUT APPAR OUTPUT ($BILLION) % CHANGE OUTPUT MOTOR OUTPUT ($BILLION) % CHANGE OUTPUT BASE MODEL 2020 3078.0223
0.10% 2009.5373
0.11% 18.6605
0.03% SCNRIO2 3070.0183
BASE MODEL 6568.5732
SCNRIO2 6553.2078
-0.26% 2010.4295
0.04% 18.7119
0.28% 2050 0.11% 4325.2331
0.12% 27.9673
0.04% -0.23% 4330.7327
0.13% 28.0539
0.31% 1.0001
1.0000
1.0000
1.0001
1.0001
1.0000
1.0001
1.0000
1.0000
1.0002
1.0001
0.9111
1.0002
1.0002
1.0171
1.0006
1.0002
1.0001
1.0001
1.0001
1.0000
1.0001
1.0001
1.0001
1.0001
1.0001
1.0001
1.0000
0.9999
0.9088
1.0000
1.0001
1.0126
1.0004
1.0000
1.0000
6.2086
0.08% 36.0105
0.07% 1.0965
-0.07% 39.3048
0.07% 15.6834
0.01% 11.9979
-0.02% 40.4675
0.05% 9.0494
0.02% 13.8359
-0.03% 30.2836
0.22% 39.3028
0.01% 2.0905
-0.01% 92.9579
0.14% 25.9513
0.20% 18.2243
0.23% 5.7836
-6.84% 32.6693
-9.28% 1.1419
4.15% 34.7300
-11.64% 15.3455
-2.15% 12.2159
1.82% 40.6323
0.41% 9.1111
0.68% 13.7330
-0.74% 31.3101
3.39% 39.2798
-0.06% 2.0918
0.06% 99.2793
6.80% 27.6314
6.47% 18.0770
-0.81% 7.6887
0.07% 57.4093
0.08% 2.6396
-0.09% 39.2540
0.11% 63.6368
0.02% 19.1419
-0.02% 87.0335
0.05% 19.4495
0.04% 29.7408
-0.05% 64.9941
0.24% 84.2137
0.02% 4.6502
-0.02% 200.2299
0.17% 55.8814
0.25% 39.3478
0.24% 7.0685
-8.07% 50.5293
-11.98% 2.7839
5.47% 30.4067
-22.54% 61.6306
-3.15% 19.6556
2.68% 87.2374
0.23% 19.5371
0.45% 29.5942
-0.49% 67.2368
3.45% 84.1389
-0.09% 4.6547
0.10% 214.2155
6.98% 59.8357
7.08% 39.0798
-0.68%
23

Scenario 3 incorporates fuel efficiency improvements in light-duty vehicles, substantial penetration of light-duty fuel cell vehicles, and again diesel blends of GTL or FTD fuels. This scenario was constructed to level demand for gasoline and diesel fuels to 2002 levels (about 17.3 billion g.g.e). As in Scenario 2, all new LDVs starting in 2008 would have ACEEE advanced fuel economy technologies. FTD would also be blended into all diesel fuels. Fuel cell vehicles using compressed hydrogen were then introduced to maintain and level out gasoline and diesel demand to 2002 levels. In other words, the reduction in demand from ACEEE technologies, plus the displacement of diesel from FTD blends, plus the displacement of gasoline from hydrogen fuel cells completely offsets the growth in demand from 2002 to 2050. Obviously, this is a very aggressive scenario and was selected as one of the upper bounding cases. Table 6-4 shows our estimates of the economic inputs to the modeling. Costs to households are 3 to 4 times higher than in the previous scenarios; a hydrogen industry develops; and the refining industry loses revenue to foreign suppliers of FTD (could be the same energy company), customers with more efficient gasoline vehicles, and new hydrogen industry (also could be the same energy companies).
Changes in Consumer Expenditures
Cost Household (inc. vehicle cost) Household (inc. FCV cost Household (inc. PZEV cost) Commercial (inc. GTL-diesel cost) Household (inc. H 2 cost) Total Cost Benefits Household
Million 2002 $ 2020 2050 Changes in Sector Revenue
5,680 945 443 125 776 10,463 Benefit Vehicle Mfg. (inc. vehicle revenue) 1,133 322 Vehicle Mfg. (inc. FCV revenue) Vehicle Mfg. (inc. PZEV revenue) 146 Foreign GTL Producer (inc. revenue) 8,718 Hydrogen industry (inc. revenue) California Excise Tax (inc. H 2 revenue) 7,970 Federal Excise Tax (inc. H 2 revenue) 20,782 Total Benefits 8,269 26,170 Cost Refiners
Million 2002 $ 2020 2050
5,680 10,463 945 443 125 673 52 52 1,133 322 146 7,609 554 554 7,970 20,782 6,454 20,425
24

(dec. gasoline expenditure Total Benefits (decrease in revenue) California Excise Tax (dec. revenue) Federal Excise Tax (dec. revenue) 8,269 26,170 Total Costs 908 908 2,872 2,872 8,269 26,170 Scenario 3 code is similar to the previous ones, but with additional lines to model hydrogen displacing gasoline. The cost of consumer transportation (CTRNS) increases by 90% of projected consumer cost. These additional costs are inserted such that the new, higher amount of consumer transportation spending is expressed as the appropriate multiple of old spending.
3 4 The cost of industrial engines increases by 10% of the projected consumer cost, plus the commercial costs. These additional costs are inserted such that the new, higher amount of industrial spending on engines is expressed as the appropriate multiple of old spending.
3 5 90% of the projected savings from increased fuel efficiency accrue to consumers. These savings are inserted such that the new, lower amount of consumer fuel spending is expressed as the appropriate fraction of old spending.
3 6 10% of the projected savings from increased fuel efficiency accrue to industry. These savings are inserted such that the new, lower amount of industrial spending on fuel is expressed as the appropriate multiple of old spending.
37 This scenario, unlike the others, includes expenditures on hydrogen fuel via the chemical (CHEM) sector that displaces money previously spent on the fossil fuels provided by the energy and mining (ENMIN) sector.
38 The table below compares selected results for base model and Scenario 3 runs of E-DRAM in both 2020 and 2050. Results show that Scenario 3 slightly reduces state output (by 0.28% in 2020 and 0.26% in 2050) while leaving state personal income roughly within the bounds of model calibration. Real personal income remains essentially constant while output falls because of increased consumer purchasing power due to improved fuel efficiency. Results indicate that the price of consumer fuel – interpreted as the price of vehicle miles traveled – is roughly 8% lower in 2020 and 12% lower 2050 under Scenario 3 than in base. Increased fuel efficiency reduces demand for refined petroleum products. E-DRAM predicts petroleum sector output being 10% lower in 2020 and roughly 30% lower in 2050 under Scenario 4our vs. base. Decreased petroleum sector output – plus fuel displacement – adversely affects upstream crude oil suppliers. The model predicts energy and mining sector output being roughly 7% lower in 2020 and 18% lower in 2050 under Scenario 3 than base. 34 REG16(I,'CTRNS') = (SUM(J, SAM(J,'CTRNS')) + .9*7.193)/SUM(J, SAM(J,'CTRNS')); 35 REG1('ENGIN',I) = (SUM(J,SAM('ENGIN',J)) + .1*7.193 + .125)/SUM(J,SAM('ENGIN',J)); 36 REG16(I,'CFUEL') = (SUM(J, SAM(J,'CFUEL')) - .9*8.269 )/SUM(J, SAM(J,'CFUEL')); 37 REG1('PETRO',I) = (SUM(J,SAM('PETRO',J)) - .1*8.269)/SUM(J,SAM('PETRO',J)); 38 REG1('CHEMS','PETRO') = (SAM('CHEMS','PETRO') + .776) / SAM('CHEMS','PETRO'); REG1('ENMIN','PETRO') = (SAM('ENMIN','PETRO') - .776) / SAM('ENMIN','PETRO');
25

Money freed from fuel expenditure is spent in other sectors. Scenario 3 raises food sector output by 6% and 9% over base in 2020 and 2050 respectively, while raising apparel sector output by roughly 5% and 9% over base in 2020 and 2050 respectively. Sectors such as motor vehicle manufacturing that rely heavily on combustion engine inputs, see costs rise; thus their prices rise and output falls. The price of consumer transportation rises 2.7% and 2.1% in 2020 and 2050 respectively, while motor vehicle sector output falls 1.1-1.2%.
26

CA OUTPUT ($BILLION) % CHANGE CA OUTPUT CA PERSONAL INCOME ($BILLION) % CHANGE CA PERS. INC.
LABOR DEMAND (MILLIONS) % CHNGE LABOR DEMAND PRICE OF CFOOD PRICE OF CHOME PRICE OF CFUEL PRICE OF CFURN PRICE OF CCLOTH PRICE OF CTRANS PRICE OF CMED PRICE OF CAMUS PRICE OF COTHR ENMIN OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS PETRO OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS ENGIN OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS CHEMS OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS FOODS OUTPUT ($BILLION) % CHANGE OUTPUT APPAR OUTPUT ($BILLION) % CHANGE OUTPUT MOTOR OUTPUT ($BILLION) % CHANGE OUTPUT BASE MODEL 2020 3078.0223
0.10% 2009.5373
0.11% 18.6605
0.03% SCNRIO3 3069.4120
BASE MODEL 6568.5732
SCNRIO3 6551.2810
-0.28% 2006.5412
-0.15% 18.6841
0.13% 2050 0.11% 4325.2331
0.12% 27.9673
0.04% -0.26% 4330.4291
0.12% 28.0763
0.39% 1.0001
1.0000
1.0000
1.0001
1.0001
1.0000
1.0001
1.0000
1.0000
1.0013
1.0008
0.9215
1.0011
1.0011
1.0271
1.0020
1.0013
1.0008
1.0001
1.0001
1.0000
1.0001
1.0001
1.0001
1.0001
1.0001
1.0001
1.0013
1.0008
0.8801
1.0011
1.0011
1.0208
1.0021
1.0012
1.0008
6.2086
0.08% 36.0105
0.07% 1.0965
-0.07% 39.3048
0.07% 15.6834
0.01% 11.9979
-0.02% 40.4675
0.05% 9.0494
0.02% 13.8359
-0.03% 30.2836
0.22% 39.3028
0.01% 2.0905
-0.01% 92.9579
0.14% 25.9513
0.20% 18.2243
0.23% 5.7448
-7.47% 32.5922
-9.49% 1.1430
4.25% 35.3868
-9.97% 15.3992
-1.81% 12.1807
1.52% 40.6730
0.51% 9.1578
1.20% 13.6559
-1.30% 32.0653
5.88% 39.3585
0.14% 2.0872
-0.16% 98.4497
5.91% 27.1334
4.55% 18.0142
-1.15% 7.6887
0.07% 57.4093
0.08% 2.6396
-0.09% 39.2540
0.11% 63.6368
0.02% 19.1419
-0.02% 87.0335
0.05% 19.4495
0.04% 29.7408
-0.05% 64.9941
0.24% 84.2137
0.02% 4.6502
-0.02% 200.2299
0.17% 55.8814
0.25% 39.3478
0.24% 6.3197
-17.81% 43.5417
-24.16% 2.9601
12.14% 27.6640
-29.53% 61.1013
-3.98% 19.7960
3.42% 87.1527
0.14% 19.6373
0.97% 29.4282
-1.05% 75.5236
16.20% 84.3541
0.17% 4.6417
-0.18% 218.8242
9.29% 61.0011
9.16% 38.8744
-1.20%
27

Scenario 4 is similar to 3 but even more aggressive with the introduction of all hybrid technologies starting in all light-duty vehicles in 2008. This case is based on ACEEE — full hybrid technologies and costs. The scenario also includes FTD blends. Table 6-5 presents our estimates of the costs and benefits for this scenario in 2002 and 2050. Here the reduction in fuel costs offset the higher vehicle costs.
Changes in Consumer Expenditures Million 2002 $ 2020 2050 Changes in Sector Revenue
Cost Household (inc. vehicle cost) Household (inc. PZEV cost) Commercial (inc. GTL-diesel cost) 13,033 21,096 Benefit Vehicle Mfg. (inc. vehicle revenue) 501 812 125 146 Vehicle Mfg. (inc. PZEV revenue) Foreign GTL Producer (inc. revenue) Total Cost Benefits 13,660 22,054 Consumer (dec. gasoline expenditure 12,533 29,896 Total Benefits Cost Refiners (decrease in revenue) California Excise Tax (dec. revenue) Federal Excise Tax (dec. revenue) Total Benefits 12,533 29,896 Total Costs
Million 2002 $ 2020 2050
13,033 21,096 501 125 1,376 1,376 812 146 13,660 22,054 9,782 23,333 3,281 3,281 12,533 29,896 Scenario 3 code is similar to the previous ones, but with additional lines to model hydrogen displacing gasoline. The cost of consumer transportation (CTRNS) increases by 90% of projected consumer cost. These additional costs are inserted such that the new, higher amount of consumer transportation spending is expressed as the appropriate multiple of old spending.
3 9 The cost of industrial engines increases by 10% of the projected consumer cost, plus the commercial costs. These additional costs are inserted such that the new, higher amount of industrial spending on engines is expressed as the appropriate multiple of old spending.
4 0 39 REG16(I,'CTRNS') = (SUM(J, SAM(J,'CTRNS')) + .9*13.534)/SUM(J, SAM(J,'CTRNS'));
28

90% of the projected savings from increased fuel efficiency accrue to consumers. These savings are inserted such that the new, lower amount of consumer fuel spending is expressed as the appropriate fraction of old spending.
4 1 10% of the projected savings from increased fuel efficiency accrue to industry. These savings are inserted such that the new, lower amount of industrial spending on fuel is expressed as the appropriate multiple of old spending.
42 On a more technical note, since any model changes not overwritten from one scenario loop to the next remain in effect, fuel displacement code from Scenario 3 must be replaced with code restoring the appropriate parameters to their default settings.
43 The table below compares selected results for base model and Scenario 4 runs of E-DRAM in both 2020 and 2050. Results show that Scenario 4 slightly reduces state output (by 0.50% in 2020 and 0.46% in 2050). State personal income also falls slightly vs. the base cases, by 0.42% in 2020 and 0.16% in 2050. Results indicate that the price of consumer fuel – interpreted as the price of vehicle miles traveled – is roughly 12% lower in 2020 and 14% lower in 2050 under Scenario 4 than base. Increased fuel efficiency reduces the demand for refined petroleum products. E-DRAM predicts petroleum sector output being 15% lower in 2020 and 33% lower in 2050 under Scenario 4 vs. base. Decreased petroleum sector output adversely affects upstream crude oil suppliers. The model predicts energy and mining sector output being 10% lower in 2020 and 13% lower in 2050 under scenario four than base. Money freed from fuel expenditure is spent in other sectors. Scenario 4 raises food sector output by 9% and 11% over base in 2020 and 2050 respectively, while raising apparel sector output by 6% and 9% over base in 2020 and 2050 respectively. Sectors such as motor vehicle manufacturing that rely heavily on combustion engine inputs, see costs rise; thus their prices rise and output falls. The price of consumer transportation rises roughly 5% and 4% in 2020 and 2050 respectively, while motor vehicle sector output falls roughly 2% and 1.7% in those same times. 40 REG1('ENGIN',I) = (SUM(J,SAM('ENGIN',J)) + .1*13.534 + .125)/SUM(J,SAM('ENGIN',J)); 41 REG16(I,'CFUEL') = (SUM(J, SAM(J,'CFUEL')) - .9*12.533 )/SUM(J, SAM(J,'CFUEL')); 42 REG1('PETRO',I) = (SUM(J,SAM('PETRO',J)) - .1*12.533)/SUM(J,SAM('PETRO',J)); 43 REG1('PETRO',I) = (SUM(J,SAM('PETRO',J)) - .1*12.533)/SUM(J,SAM('PETRO',J));
29

CA OUTPUT ($BILLION) % CHANGE CA OUTPUT CA PERSONAL INCOME ($BILLION) % CHANGE CA PERS. INC.
LABOR DEMAND (MILLIONS) % CHNGE LABOR DEMAND PRICE OF CFOOD PRICE OF CHOME PRICE OF CFUEL PRICE OF CFURN PRICE OF CCLOTH PRICE OF CTRANS PRICE OF CMED PRICE OF CAMUS PRICE OF COTHR ENMIN OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS PETRO OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS ENGIN OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS CHEMS OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS FOODS OUTPUT ($BILLION) % CHANGE OUTPUT APPAR OUTPUT ($BILLION) % CHANGE OUTPUT MOTOR OUTPUT ($BILLION) % CHANGE OUTPUT 2020 TODAY 3078.0223
SCNRIO4 3062.4866
0.10% 2009.5373
0.11% 18.6605
0.03% -0.50% 2001.0251
-0.42% 18.6726
0.06% 2050 TODAY 6568.5732
SCNRIO4 6538.4894
0.11% 4325.2331
0.12% 27.9673
0.04% -0.46% 4318.1160
-0.16% 28.0382
0.25% 1.0001
1.0000
1.0000
1.0001
1.0001
1.0000
1.0001
1.0000
1.0000
1.0026
1.0018
0.8818
1.0022
1.0023
1.0513
1.0038
1.0027
1.0017
1.0001
1.0001
1.0000
1.0001
1.0001
1.0001
1.0001
1.0001
1.0001
1.0018
1.0012
0.8636
1.0015
1.0016
1.0382
1.0029
1.0018
1.0012
6.2086
0.08% 36.0105
0.07% 1.0965
-0.07% 39.3048
0.07% 15.6834
0.01% 11.9979
-0.02% 40.4675
0.05% 9.0494
0.02% 13.8359
-0.03% 30.2836
0.22% 39.3028
0.01% 2.0905
-0.01% 92.9579
0.14% 25.9513
0.20% 18.2243
0.23% 5.6084
-9.67% 31.8337
-11.60% 1.1542
5.27% 33.5161
-14.73% 15.2814
-2.56% 12.2582
2.17% 40.8046
0.83% 9.2482
2.20% 13.5091
-2.36% 31.6679
4.57% 39.4178
0.29% 2.0838
-0.32% 101.3527
9.03% 27.5086
6.00% 17.8553
-2.02% 7.6887
0.07% 57.4093
0.08% 2.6396
-0.09% 39.2540
0.11% 63.6368
0.02% 19.1419
-0.02% 87.0335
0.05% 19.4495
0.04% 29.7408
-0.05% 64.9941
0.24% 84.2137
0.02% 4.6502
-0.02% 200.2299
0.17% 55.8814
0.25% 39.3478
0.24% 6.7220
-12.57% 47.5359
-17.20% 2.8549
8.16% 26.4558
-32.60% 60.7897
-4.47% 19.8796
3.85% 87.4671
0.50% 19.7580
1.59% 29.2304
-1.72% 68.3594
5.18% 84.3420
0.15% 4.6424
-0.17% 221.4745
10.61% 60.8908
8.96% 38.6851
-1.68%
30

Comparing effects across scenarios in 2020 and 2050 reveals the following. First, gains in fuel efficiency reduce the price of vehicle miles traveled. Scenarios 1, 2, and 4 reflect progressively more fuel efficient technologies. The scenarios implement static fuel cost saving of roughly $3.3 billion, $9.3 billion, and $12.5 billion respectively and E-DRAM predicts the price of CFUEL falling sequentially by scenario to 97%, 91%, and 88% of its base level. Second, while gains in fuel efficiency, which translate into lower petroleum consumption and production, appear to reduce nominal state output by 0.1-0.5% depending on the scenario, real state income remains nearly constant because of aggregate price level deflation due lower fuel costs. Real SPI falls by more than calibration error only under Scenario 4 / 2020 – the only permutation in which projected engine costs outweigh fuel savings. None of the strategies appears to have significant negative impacts on the state economy as a whole. The cost of building and buying more efficient engines is generally offset by their cheaper operating costs. This said, however, adjustments in the energy related sectors are significant. In 2020, ENMIN and PETRO sector output fall 2-10% and 4-15% below base respectively, depending on the scenario.
31

2020 CA OUTPUT ($BILLION) % CHANGE CA OUTPUT CA PERSONAL INCOME ($BILLION) % CHANGE CA PERS. INC.
LABOR DEMAND (MILLIONS) % CHNGE LABOR DEMAND PRICE OF CFOOD PRICE OF CHOME PRICE OF CFUEL PRICE OF CFURN PRICE OF CCLOTH PRICE OF CTRANS PRICE OF CMED PRICE OF CAMUS PRICE OF COTHR ENMIN OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS PETRO OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS ENGIN OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS CHEMS OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS FOODS OUTPUT ($BILLION) % CHANGE OUTPUT APPAR OUTPUT ($BILLION) % CHANGE OUTPUT MOTOR OUTPUT ($BILLION) % CHANGE OUTPUT BASE MODEL 3078.0223
0.10% SCNRIO1 3074.9243
-0.10% SCNRIO2 3070.0183
-0.26% SCNRIO3 3069.4120
-0.28% SCNRIO4 3062.4866
-0.50% 2009.5373
0.11% 18.6605
2009.5213
0.00% 18.6767
2010.4295
0.04% 18.7119
2006.5412
-0.15% 18.6841
2001.0251
-0.42% 18.6726
0.03% 1.0001
1.0000
1.0000
1.0001
1.0001
1.0000
1.0001
1.0000
1.0000
0.09% 1.0001
1.0000
0.9687
1.0001
1.0001
1.0072
1.0002
1.0001
1.0000
0.28% 1.0002
1.0001
0.9111
1.0002
1.0002
1.0171
1.0006
1.0002
1.0001
0.13% 1.0013
1.0008
0.9215
1.0011
1.0011
1.0271
1.0020
1.0013
1.0008
0.06% 1.0026
1.0018
0.8818
1.0022
1.0023
1.0513
1.0038
1.0027
1.0017
6.2086
0.08% 36.0105
0.07% 1.0965
-0.07% 39.3048
0.07% 15.6834
0.01% 11.9979
-0.02% 40.4675
0.05% 9.0494
0.02% 13.8359
-0.03% 30.2836
0.22% 39.3028
0.01% 2.0905
-0.01% 92.9579
0.14% 25.9513
0.20% 18.2243
0.23% 6.0575
-2.43% 34.8290
-3.28% 1.1122
1.43% 37.6902
-4.11% 15.5646
-0.76% 12.0739
0.63% 40.5818
0.28% 9.0815
0.35% 13.7822
-0.39% 30.6482
1.20% 39.2943
-0.02% 2.0910
0.02% 95.1127
2.32% 26.4969
2.10% 18.1613
-0.35% 5.7836
-6.84% 32.6693
-9.28% 1.1419
4.15% 34.7300
-11.64% 15.3455
-2.15% 12.2159
1.82% 40.6323
0.41% 9.1111
0.68% 13.7330
-0.74% 31.3101
3.39% 39.2798
-0.06% 2.0918
0.06% 99.2793
6.80% 27.6314
6.47% 18.0770
-0.81% 5.7448
-7.47% 32.5922
-9.49% 1.1430
4.25% 35.3868
-9.97% 15.3992
-1.81% 12.1807
1.52% 40.6730
0.51% 9.1578
1.20% 13.6559
-1.30% 32.0653
5.88% 39.3585
0.14% 2.0872
-0.16% 98.4497
5.91% 27.1334
4.55% 18.0142
-1.15% 5.6084
-9.67% 31.8337
-11.60% 1.1542
5.27% 33.5161
-14.73% 15.2814
-2.56% 12.2582
2.17% 40.8046
0.83% 9.2482
2.20% 13.5091
-2.36% 31.6679
4.57% 39.4178
0.29% 2.0838
-0.32% 101.3527
9.03% 27.5086
6.00% 17.8553
-2.02%
32

2050 CA OUTPUT ($BILLION) % CHANGE CA OUTPUT CA PERSONAL INCOME ($BILLION) % CHANGE CA PERS. INC.
LABOR DEMAND (MILLIONS) % CHNGE LABOR DEMAND PRICE OF CFOOD PRICE OF CHOME PRICE OF CFUEL PRICE OF CFURN PRICE OF CCLOTH PRICE OF CTRANS PRICE OF CMED PRICE OF CAMUS PRICE OF COTHR ENMIN OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS PETRO OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS ENGIN OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS CHEMS OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS FOODS OUTPUT ($BILLION) % CHANGE OUTPUT APPAR OUTPUT ($BILLION) % CHANGE OUTPUT MOTOR OUTPUT ($BILLION) % CHANGE OUTPUT BASE MODEL 6568.5732
0.11% SCNRIO1 6557.2797
-0.17% SCNRIO2 6553.2078
-0.23% SCNRIO3 6551.2810
-0.26% SCNRIO4 6538.4894
-0.46% 4325.2331
0.12% 27.9673
4329.6794
0.10% 28.0326
4330.7327
0.13% 28.0539
4330.4291
0.12% 28.0763
4318.1160
-0.16% 28.0382
0.04% 1.0001
1.0001
1.0000
1.0001
1.0001
1.0001
1.0001
1.0001
1.0001
0.23% 1.0000
0.9999
0.9324
1.0000
1.0000
1.0095
1.0003
0.9999
0.9999
0.31% 1.0000
0.9999
0.9088
1.0000
1.0001
1.0126
1.0004
1.0000
1.0000
0.39% 1.0013
1.0008
0.8801
1.0011
1.0011
1.0208
1.0021
1.0012
1.0008
0.25% 1.0018
1.0012
0.8636
1.0015
1.0016
1.0382
1.0029
1.0018
1.0012
7.6887
0.07% 57.4093
0.08% 2.6396
-0.09% 39.2540
0.11% 63.6368
0.02% 19.1419
-0.02% 87.0335
0.05% 19.4495
0.04% 29.7408
-0.05% 64.9941
0.24% 84.2137
0.02% 4.6502
-0.02% 200.2299
0.17% 55.8814
0.25% 39.3478
0.24% 7.2328
-5.93% 52.2725
-8.95% 2.7452
4.00% 32.6620
-16.79% 62.1426
-2.35% 19.5219
1.99% 87.2217
0.22% 19.5153
0.34% 29.6307
-0.37% 66.6697
2.58% 84.1483
-0.08% 4.6542
0.09% 210.4874
5.12% 58.7842
5.19% 39.1508
-0.50% 7.0685
-8.07% 50.5293
-11.98% 2.7839
5.47% 30.4067
-22.54% 61.6306
-3.15% 19.6556
2.68% 87.2374
0.23% 19.5371
0.45% 29.5942
-0.49% 67.2368
3.45% 84.1389
-0.09% 4.6547
0.10% 214.2155
6.98% 59.8357
7.08% 39.0798
-0.68% 6.3197
-17.81% 43.5417
-24.16% 2.9601
12.14% 27.6640
-29.53% 61.1013
-3.98% 19.7960
3.42% 87.1527
0.14% 19.6373
0.97% 29.4282
-1.05% 75.5236
16.20% 84.3541
0.17% 4.6417
-0.18% 218.8242
9.29% 61.0011
9.16% 38.8744
-1.20% 6.7220
-12.57% 47.5359
-17.20% 2.8549
8.16% 26.4558
-32.60% 60.7897
-4.47% 19.8796
3.85% 87.4671
0.50% 19.7580
1.59% 29.2304
-1.72% 68.3594
5.18% 84.3420
0.15% 4.6424
-0.17% 221.4745
10.61% 60.8908
8.96% 38.6851
-1.68%
33

Sensitivity analysis – examining the behavior of a model in response to key input changes – is a good way to assess a model's properties and bolster confidence in its results. E-DRAM's predecessor, DRAM, has undergone extensive sensitivity analysis, as documented in Berck, et. al. (Summer 1996). For purposes of this project, it is useful to examine E-DRAM's when parameters governing consumers' sensitivity to fuel prices, petroleum imports as a function of domestic price, and overall economic performance as a function of energy prices are changed. To this end, the following experiments are performed.
Changing the own-price elasticity of demand CFUEL changes consumers' sensitivity to fuel price changes. More specifically, lowering this parameter to -0.77 (from its default setting of -0.2) makes consumers respond to a 1.0% decrease in the price of fuel price by demanding 0.77% (rather than 0.2%) more fuel. Economists describe the elasticity of -.77 as more elastic than the elasticity of -.2. Running the 2020 version of E-DRAM with this new (vs. old) elasiticity imposed yields results listed in the gray (vs. white) columns. The contrast is as expected. The more sensitive consumers are to fuel price changes, the less they cut back fuel consumption in response to increased fuel efficiency. This is because fuel efficiency gains trigger two opposing effects. One is a decreased demand for fuel since less is needed to produce the same number of vehicle miles traveled. The other is an increased demand for vehicle miles traveled because they're cheaper. It's the low-price elasticity of demand that governs the size of this second response, i.e., raising this parameter's (absolute) value means a greater increase in the quantity demanded per any given price decrease. With more elastic of demand for CFUEL, statewide impacts of the scenarios being considered are dampened slightly. In Scenario 4, for example, state output declines by 0.2% rather than 0.5% and real personal income falls by 0.1% rather than 0.4%. With consumers buying relatively more fuel, ENMIN and PETRO sector output decline by only 4.6% and 7.4% respectively, rather than by 9.7% and 14.7% respectively. Demand for complimentary products thus rises relative to the base model, e.g., ENGIN sector output increases 1.3% rather than 0.8%. Relatively less spending is shifted to fuel substitutes like food and apparel, e.g., FOODS and APPPAR sector output increase by 8.3% (vs. 9.0% in base) and 3.6% (vs. 6.0% in base) respectively.
34

2020 CA OUTPUT ($BILLION) % CHANGE CA OUTPUT CA PERS. INC. ($BIL.) % CHNGE CA PERS. INC.
LAB. DEMAND (MIL.) BASE MODEL SCNRIO1 SCNRIO1 SCNRIO2 SCNRIO2 SCNRIO3 SCNRIO3 SCNRIO4 SCNRIO4 3078.022 3074.924 3076.657 3070.018 3075.484 3069.412 3074.329 3062.487 3070.572
0.10% -0.10% -0.04% -0.26% -0.08% -0.28% -0.12% -0.50% -0.24% 2009.537 2009.521 2010.283 2010.429 2013.756 2006.541 2009.407 2001.025 2006.661
0.11% 0.00% 0.04% 0.04% 0.21% -0.15% -0.01% -0.42% -0.14% 18.661
18.677
18.677
18.712
18.719
18.684
18.690
18.673
18.688
% CHNGE LAB. DEMAND PRICE OF CFOOD PRICE OF CHOME PRICE OF CFUEL PRICE OF CFURN PRICE OF CCLOTH PRICE OF CTRANS PRICE OF CMED PRICE OF CAMUS PRICE OF COTHR 0.03% 1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
0.09% 1.000
1.000
0.969
1.000
1.000
1.007
1.000
1.000
1.000
0.09% 1.000
1.000
0.969
1.000
1.000
1.007
1.000
1.000
1.000
0.28% 1.000
1.000
0.911
1.000
1.000
1.017
1.001
1.000
1.000
0.31% 0.999
0.999
0.911
0.999
0.999
1.016
0.999
0.999
0.999
0.13% 1.001
1.001
0.922
1.001
1.001
1.027
1.002
1.001
1.001
0.16% 1.000
1.000
0.922
1.000
1.000
1.026
1.001
1.000
1.000
0.06% 1.003
1.002
0.882
1.002
1.002
1.051
1.004
1.003
1.002
0.15% 1.001
1.001
0.882
1.001
1.001
1.050
1.002
1.001
1.000
ENMIN OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS PETRO OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS ENGIN OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS CHEMS OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS FOODS OUTPUT ($BILLION) % CHANGE OUTPUT APPAR OUTPUT ($BILLION) % CHANGE OUTPUT MOTOR OUTPUT ($BILLION) % CHANGE OUTPUT 6.209
0.08% 36.011
0.07% 1.096
-0.07% 39.305
0.07% 15.683
0.01% 11.998
-0.02% 40.468
0.05% 9.049
0.02% 13.836
-0.03% 30.284
0.22% 39.303
0.01% 2.090
-0.01% 92.958
0.14% 25.951
0.20% 18.224
0.23% 6.058
-2.43% 34.829
-3.28% 1.112
1.43% 37.690
-4.11% 15.565
-0.76% 12.074
0.63% 40.582
0.28% 9.081
0.35% 13.782
-0.39% 30.648
1.20% 39.294
-0.02% 2.091
0.02% 95.113
2.32% 26.497
2.10% 18.161
-0.35% 6.134
-1.19% 35.418
-1.65% 1.104
0.73% 38.466
-2.14% 15.597
-0.55% 12.053
0.46% 40.619
0.37% 9.076
0.29% 13.792
-0.32% 30.602
1.05% 39.278
-0.06% 2.092
0.07% 94.919
2.11% 26.323
1.43% 18.190
-0.19% 5.784
-6.84% 32.669
-9.28% 1.142
4.15% 34.730
-11.64% 15.345
-2.15% 12.216
1.82% 40.632
0.41% 9.111
0.68% 13.733
-0.74% 31.310
3.39% 39.280
-0.06% 2.092
0.06% 99.279
6.80% 27.631
6.47% 18.077
-0.81% 6.008
-3.24% 34.270
-4.83% 1.120
2.11% 36.855
-6.23% 15.431
-1.61% 12.160
1.35% 40.761
0.72% 9.089
0.44% 13.770
-0.48% 31.221
3.09% 39.219
-0.21% 2.095
0.23% 98.760
6.24% 27.163
4.67% 18.168
-0.31% 5.745
-7.47% 32.592
-9.49% 1.143
4.25% 35.387
-9.97% 15.399
-1.81% 12.181
1.52% 40.673
0.51% 9.158
1.20% 13.656
-1.30% 32.065
5.88% 39.358
0.14% 2.087
-0.16% 98.450
5.91% 27.133
4.55% 18.014
-1.15% 5.945
-4.25% 34.022
-5.52% 1.123
2.42% 37.328
-5.03% 15.476
-1.32% 12.131
1.11% 40.786
0.79% 9.139
0.99% 13.687
-1.07% 32.027
5.76% 39.307
0.01% 2.090
-0.01% 97.975
5.40% 26.707
2.91% 18.095
-0.71% 5.608
-9.67% 31.834
-11.60% 1.154
5.27% 33.516
-14.73% 15.281
-2.56% 12.258
2.17% 40.805
0.83% 9.248
2.20% 13.509
-2.36% 31.668
4.57% 39.418
0.29% 2.084
-0.32% 101.353
9.03% 27.509
6.00% 17.855
-2.02% 5.921
-4.64% 34.000
-5.58% 1.123
2.45% 36.401
-7.39% 15.394
-1.85% 12.184
1.55% 41.005
1.33% 9.213
1.81% 13.566
-1.95% 31.581
4.28% 39.321
0.05% 2.089
-0.05% 100.663
8.29% 26.885
3.60% 17.991
-1.28%
35

Lowering the elasticity of imports with respect to domestic price (ETAM) makes the quantity of goods imported less sensitive to domestic price changes. Changing ETAM for the petroleum sector to 0.1 (from its default setting of 2) means that a 1.0% decrease in the domestic price of petroleum decreases imports of refined petroleum by 0.1% (rather than 2.0%).and from 4.0 to 1.0 for the energy and mining sector With these parameter changes, Similarly, changing ETAM for the ENMIN sector to 1 (from its default setting of 4) means that a 1% decrease in the domestic price of crude oil will decrease imports of crude oil by 1.0% (rather than 4.0%). The parameter changes outlined above cause some domestic PETRO and ENMIN sector production to be being supplanted by imports, as expected. The table below shows results from running the 2020 version of E-DRAM with new (vs. old) elasticities listed in the gray (vs. white) columns. While statewide effects aren't appreciably different with these new parameter settings, adverse impacts on the ENMIN and PETRO sectors are amplified as falling demand is compounded by rising imports. This compounding is greatest in the ENMIN sector where domestic output falls 7.3% (vs. 2.4%) in Scenario 1, 21.1% (vs. 6.8%) in Scenario 2, 21.9% (vs. 7.5%) in Scenario 3, and 27.6% (vs. 9.7%) in Scenario 4. Conversely, if the elasticities of trade were increased, or the domestic elasticity of supply were decreased, domestic output would be less sensitive to the scenarios and state output and personal income would be higher.
36

2020 CA OUTPUT ($BILLION) % CHANGE CA OUTPUT CA PERS. INC. ($BIL.) % CHNGE CA PERS. INC.
LAB. DEMAND (MIL.) BASE MODEL SCNRIO1 SCNRIO1 SCNRIO2 SCNRIO2 SCNRIO3 SCNRIO3 SCNRIO4 SCNRIO4 3078.022 3074.924 3074.649 3070.018 3069.005 3069.412 3068.447 3062.487 3061.123
0.10% -0.10% -0.11% -0.26% -0.29% -0.28% -0.31% -0.50% -0.55% 2009.537 2009.521 2009.361 2010.429 2009.858 2006.541 2005.985 2001.025 2000.271
0.11% 0.00% -0.01% 0.04% 0.02% -0.15% -0.18% -0.42% -0.46% 18.661
18.677
18.677
18.712
18.711
18.684
18.684
18.673 18.67164
% CHNGE LAB. DEMAND PRICE OF CFOOD PRICE OF CHOME PRICE OF CFUEL PRICE OF CFURN PRICE OF CCLOTH PRICE OF CTRANS PRICE OF CMED PRICE OF CAMUS PRICE OF COTHR 0.03% 1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
0.09% 1.000
1.000
0.969
1.000
1.000
1.007
1.000
1.000
1.000
0.09% 1.000
1.000
0.968
1.000
1.000
1.007
1.000
1.000
1.000
0.28% 1.000
1.000
0.911
1.000
1.000
1.017
1.001
1.000
1.000
0.27% 1.000
1.000
0.910
1.000
1.000
1.017
1.000
1.000
1.000
0.13% 1.001
1.001
0.922
1.001
1.001
1.027
1.002
1.001
1.001
0.12% 1.001
1.001
0.921
1.001
1.001
1.027
1.002
1.001
1.001
0.06% 0.06% 1.003 1.002197
1.002 1.001495
0.882 0.880875
1.002 1.001886
1.002
1.00194
1.051 1.051033
1.004 1.003364
1.003 1.002307
1.002 1.001442
ENMIN OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS PETRO OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS ENGIN OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS CHEMS OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS FOODS OUTPUT ($BILLION) % CHANGE OUTPUT APPAR OUTPUT ($BILLION) % CHANGE OUTPUT MOTOR OUTPUT ($BILLION) % CHANGE OUTPUT 6.209
0.08% 36.011
0.07% 1.096
-0.07% 39.305
0.07% 15.683
0.01% 11.998
-0.02% 40.468
0.05% 9.049
0.02% 13.836
-0.03% 30.284
0.22% 39.303
0.01% 2.090
-0.01% 92.958
0.14% 25.951
0.20% 18.224
0.23% 6.058
-2.43% 34.829
-3.28% 1.112
1.43% 37.690
-4.11% 15.565
-0.76% 12.074
0.63% 40.582
0.28% 9.081
0.35% 13.782
-0.39% 30.648
1.20% 39.294
-0.02% 2.091
0.02% 95.113
2.32% 26.497
2.10% 18.161
-0.35% 5.754
-7.32% 35.060
-2.64% 1.146
4.47% 37.608
-4.32% 15.673
-0.07% 12.100
0.85% 40.587
0.29% 9.079
0.33% 13.786
-0.36% 30.661
1.25% 39.285
-0.05% 2.092
0.05% 95.145
2.35% 26.507
2.14% 18.165
-0.33% 5.784
-6.84% 32.669
-9.28% 1.142
4.15% 34.730
-11.64% 15.345
-2.15% 12.216
1.82% 40.632
0.41% 9.111
0.68% 13.733
-0.74% 31.310
3.39% 39.280
-0.06% 2.092
0.06% 99.279
6.80% 27.631
6.47% 18.077
-0.81% 4.897
-21.13% 33.336
-7.43% 1.245
13.54% 34.466
-12.31% 15.659
-0.15% 12.277
2.32% 40.645
0.44% 9.105
0.61% 13.743
-0.67% 31.336
3.48% 39.255
-0.12% 2.093
0.13% 99.343
6.87% 27.650
6.55% 18.084
-0.77% 5.745
-7.47% 32.592
-9.49% 1.143
4.25% 35.387
-9.97% 15.399
-1.81% 12.181
1.52% 40.673
0.51% 9.158
1.20% 13.656
-1.30% 32.065
5.88% 39.358
0.14% 2.087
-0.16% 98.450
5.91% 27.133
4.55% 18.014
-1.15% 4.849
-21.90% 33.298
-7.53% 1.247
13.75% 35.173
-10.51% 15.662
-0.13% 12.238
2.00% 40.685
0.54% 9.152
1.13% 13.666
-1.23% 32.087
5.96% 39.334
0.08% 2.089
-0.09% 98.512
5.98% 27.152
4.63% 18.021
-1.12% 5.608 4.494602
-9.67% 31.834 32.67667
-11.60% -9.26% 1.154 1.286606
5.27% 17.34% 33.516 33.19064
-14.73% 15.281
-2.56% -0.18% 12.258 12.32398
2.17% 40.805 40.81842
0.83% 9.248 9.240452
2.20% 13.509 13.52159
-2.36% 31.668 31.69406
4.57% 39.418 39.38798
0.29% 2.084
-0.32% 9.03% 6.00% -2.02% -27.61% -15.56% 15.6558
2.72% 0.87% 2.11% -2.27% 4.66% 0.22% 2.08549
-0.24% 101.353 101.4153
9.10% 27.509 27.52633
6.07% 17.855 17.86203
-1.99%
37

A primary motivation for decreasing petroleum dependency is limiting vulnerability to supply shocks that cause price spikes. Examining how E-DRAM assesses the impact of such spikes on the state economy – and predicting the extent to which the scenarios under consideration these impacts – is thus critical. The table below compares runs given 20% higher world ENMIN and PETRO prices (gray columns) with runs at original world prices (white columns) . Comparing "NEW MODEL" to "BASE MODEL" columns shows that E-DRAM predicts 2020 California state product being roughly $21 billion (0.7%) lower and state personal income being $22 billion (1.1%) lower when both world PETRO and ENMIN prices are 20% higher. These higher world prices nudge the price of consumer fuel (CFUEL) up 6.2%, while the price of other consumer goods remain constant or fall slightly (0.1-0.2%).
45 Domestic output in the energy and mining sector rises nearly $2.2 billion (35%) while domestic output in the petroleum sector rises $1.0 billion (2.6%) as higher world prices drive down imports in those sectors.
46 Other sectors contract in the face of world energy price inflation, e.g., output of the FOODS and APPAR sectors falls by 5.6% and 7.2% respectively. Comparing the gray and white "SCENARIO#" columns confirms the intuition that strategies to improve fuel efficiency reap greater rewards in a world with higher energy prices. Higher world prices induce greater domestic production that offsets declines in California's ENMIN and PETRO sector production triggered by demand reduction due to efficiency gains. In Scenario 4 with high world prices (vs. base model prices), for example, state output falls 0.4% (vs. 0.5%) and personal income falls 0.2% (vs. 0.4%); domestic ENMIN output falls 4.4% (vs. 9.7%) and PETRO production falls 12.3% (vs. 14.7%). 45 The price of CFUEL rises by significantly less than 20% because the CFUEL sector also includes utilities. 46 The domestic production as a share of imports is much lower in the ENMIN than in the PETRO sector.
38

2020 CA OUTPUT ($BIL.) % CHNGE OUTPUT PERS. INC. ($BIL.) % CHNGE PERS. INC.
JOBS (MIL.) % CHNGE JOBS PRICE OF CFOOD PRICE OF CHOME PRICE OF CFUEL PRICE OF CFURN PRICE OF CCLOTH PRICE OF CTRANS PRICE OF CMED PRICE OF CAMUS PRICE OF COTHR ENMIN OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS PETRO OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS ENGIN OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS CHEMS OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS FOODS OUTPUT ($BILLION) % CHANGE OUTPUT APPAR OUTPUT ($BILLION) % CHANGE OUTPUT MOTOR OUTPUT ($BILLION) % CHANGE OUTPUT BASE MODEL NEW MODEL SCNRIO1 SCNRIO1 SCNRIO2 SCNRIO2 SCNRIO3 SCNRIO3 SCNRIO4 SCNRIO4 3078.022 3057.149 3074.924 3055.703 3070.018 3052.939 3069.412 3052.433 3062.487 3046.364
0.10% -0.58% -0.10% -0.05% -0.26% -0.14% -0.28% -0.15% -0.50% -0.35% 2009.537 1987.684 2009.521 1989.172 2010.429 1992.458 2006.541 1988.392 2001.025 1984.108
0.11% -0.98% 0.00% 0.07% 0.04% 0.24% -0.15% 0.04% -0.42% -0.18% 18.661
18.536
18.677
18.558
18.712
18.605
18.684
18.577
18.673
18.571
0.03% 1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
-0.64% 1.000
1.000
1.062
1.000
1.000
1.000
0.998
0.999
0.999
0.09% 1.000
1.000
0.969
1.000
1.000
1.007
1.000
1.000
1.000
0.12% 1.000
1.000
1.030
1.000
1.000
1.008
0.998
0.999
0.999
0.28% 1.000
1.000
0.911
1.000
1.000
1.017
1.001
1.000
1.000
0.37% 1.000
1.000
0.969
1.000
1.000
1.017
0.999
0.999
0.999
0.13% 1.001
1.001
0.922
1.001
1.001
1.027
1.002
1.001
1.001
0.22% 1.001
1.000
0.979
1.001
1.001
1.027
1.000
1.000
1.000
0.06% 1.003
1.002
0.882
1.002
1.002
1.051
1.004
1.003
1.002
0.19% 1.002
1.001
0.938
1.002
1.002
1.052
1.002
1.002
1.001
6.209
0.08% 36.011
0.07% 1.096
-0.07% 39.305
0.07% 15.683
0.01% 11.998
-0.02% 40.468
0.05% 9.049
0.02% 13.836
-0.03% 30.284
0.22% 39.303
0.01% 2.090
-0.01% 92.958
0.14% 25.951
0.20% 18.224
0.23% 8.394
35.31% 34.875
-3.09% 1.136
3.51% 40.335
2.69% 14.222
-9.30% 13.361
11.34% 40.443
-0.01% 9.009
-0.42% 13.904
0.46% 28.636
-5.23% 39.601
0.77% 2.073
-0.84% 87.663
-5.56% 24.030
-7.22% 17.880
-1.67% 6.058
-2.43% 34.829
-3.28% 1.112
1.43% 37.690
-4.11% 15.565
-0.76% 12.074
0.63% 40.582
0.28% 9.081
0.35% 13.782
-0.39% 30.648
1.20% 39.294
-0.02% 2.091
0.02% 95.113
2.32% 26.497
2.10% 18.161
-0.35% 8.477
0.99% 33.762
-3.19% 1.127
-0.82% 39.238
-2.72% 13.711
-3.59% 13.405
0.33% 40.563
0.30% 9.043
0.37% 13.847
-0.41% 29.029
1.37% 39.599
0.00% 2.073
0.00% 89.805
2.44% 24.595
2.35% 17.829
-0.29% 5.784
-6.84% 32.669
-9.28% 1.142
4.15% 34.730
-11.64% 15.345
-2.15% 12.216
1.82% 40.632
0.41% 9.111
0.68% 13.733
-0.74% 31.310
3.39% 39.280
-0.06% 2.092
0.06% 99.279
6.80% 27.631
6.47% 18.077
-0.81% 8.205
-2.25% 31.738
-9.00% 1.156
1.75% 36.508
-9.49% 13.519
-4.95% 13.562
1.51% 40.643
0.50% 9.069
0.67% 13.802
-0.73% 29.766
3.95% 39.573
-0.07% 2.075
0.08% 93.999
7.23% 25.781
7.28% 17.772
-0.61% 5.745
-7.47% 32.592
-9.49% 1.143
4.25% 35.387
-9.97% 15.399
-1.81% 12.181
1.52% 40.673
0.51% 9.158
1.20% 13.656
-1.30% 32.065
5.88% 39.358
0.14% 2.087
-0.16% 98.450
5.91% 27.133
4.55% 18.014
-1.15% 8.168
-2.69% 31.701
-9.10% 1.156
1.79% 37.331
-7.45% 13.468
-5.30% 13.604
1.82% 40.674
0.57% 9.118
1.21% 13.721
-1.31% 30.572
6.76% 39.657
0.14% 2.070
-0.16% 93.241
6.36% 25.302
5.29% 17.707
-0.97% 5.608
-9.67% 31.834
-11.60% 1.154
5.27% 33.516
-14.73% 15.281
-2.56% 12.258
2.17% 40.805
0.83% 9.248
2.20% 13.509
-2.36% 31.668
4.57% 39.418
0.29% 2.084
-0.32% 101.353
9.03% 27.509
6.00% 17.855
-2.02% 8.027
-4.37% 30.946
-11.27% 1.168
2.81% 35.370
-12.31% 13.459
-5.37% 13.612
1.88% 40.828
0.95% 9.205
2.17% 13.579
-2.33% 30.161
5.32% 39.705
0.26% 2.067
-0.29% 96.095
9.62% 25.690
6.91% 17.565
-1.76%
39

In experiments where the world price of only refined petroleum rises by 20% (e.g., if refining capacity were the pressing constraint), E-DRAM behaves in much the same way as discussed above, only to a lesser degree. Comparing "BASE MODEL" and "NEW MODEL" columns shows that E-DRAM predicts 2020 California state product actually increasing slightly, as the rise in state ENMIN production triggered by a higher world crude oil price offsets declines in demand triggered by fuel efficiency gains. Other sectors contract in the face of world refined petroleum price inflation, e.g., output of the FOODS and APPAR sectors falls by 1.9% and 2.3% respectively. Comparing "SCNENARIO#" columns again indicates that strategies to improve fuel efficiency reap greater rewards when world energy prices are relatively high. With 20% higher world petroleum prices, declines in state output and employment due to the various scenarios are generally 20-50% less than they would be with lower world prices. The higher world PETRO prices bring forth greater domestic PETRO production, thus offsetting declines in California's PETRO, and by extension, ENMIN, sectors that demand reduction due to efficiency gains would otherwise have triggered. In Scenario 4 with high world prices (vs. base model prices), for example, state output falls 0.4% (vs. 0.5%) and state personal income falls 0.3% (vs. 0.4%), as domestic PETRO production falls only 9.9% (vs. 14.7%).
40

2020 CA OUTPUT ($BIL.) % CHNGE OUTPUT PERS. INC. ($BIL.) % CHNGE PERS. INC.
JOBS (MIL.) % CHNGE JOBS PRICE OF CFOOD PRICE OF CHOME PRICE OF CFUEL PRICE OF CFURN PRICE OF CCLOTH PRICE OF CTRANS PRICE OF CMED PRICE OF CAMUS PRICE OF COTHR ENMIN OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS PETRO BASE MODEL NEW MODEL SCNRIO1 SCNRIO1 SCNRIO2 SCNRIO2 SCNRIO3 SCNRIO3 SCNRIO4 SCNRIO4 3078.022 3081.352 3074.924 3080.196 3070.018 3075.686 3069.412 3075.117 3062.487 3068.329
0.10% 0.20% -0.10% -0.04% -0.26% -0.18% -0.28% -0.20% -0.50% -0.42% 2009.537 2006.100 2009.521 2007.093 2010.429 2008.506 2006.541 2004.541 2001.025 1999.308
0.11% -0.06% 0.00% 0.05% 0.04% 0.12% -0.15% -0.08% -0.42% -0.34% 18.661
0.03% 1.000
1.000
1.000
1.000
18.629
-0.14% 1.001
1.001
1.024
1.001
18.677
0.09% 1.000
1.000
0.969
1.000
18.651
0.12% 1.001
1.001
0.991
1.001
18.712
0.28% 1.000
1.000
0.911
1.000
18.688
0.32% 1.001
1.001
0.933
1.001
18.684
0.13% 1.001
1.001
0.922
1.001
18.660
0.17% 1.002
1.001
0.943
1.002
18.673
0.06% 1.003
1.002
0.882
1.002
18.650
0.11% 1.003
1.002
0.903
1.003
1.000
1.000
1.000
1.000
1.000
1.001
1.001
1.001
1.001
1.001
1.000
1.007
1.000
1.000
1.000
1.001
1.008
1.001
1.001
1.001
1.000
1.017
1.001
1.000
1.000
1.001
1.018
1.001
1.001
1.001
1.001
1.027
1.002
1.001
1.001
1.002
1.028
1.003
1.002
1.001
1.002
1.051
1.004
1.003
1.002
1.003
1.052
1.004
1.003
1.002
6.209
0.08% 36.011
0.07% 1.096
-0.07% 7.069
13.95% 38.931
8.18% 1.006
-8.29% 6.058
-2.43% 34.829
-3.28% 1.112
1.43% 6.488
-8.22% 38.768
-0.42% 1.064
5.74% 5.784
-6.84% 32.669
-9.28% 1.142
4.15% 6.241
-11.71% 36.595
-6.00% 1.090
8.29% 5.745
-7.47% 32.592
-9.49% 1.143
4.25% 6.186
-12.48% 36.388
-6.53% 1.092
8.54% 5.608
-9.67% 31.834
-11.60% 1.154
5.27% 6.076
-14.05% 35.749
-8.18% 1.100
9.34% OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS 39.305
0.07% 15.683
0.01% 11.998
-0.02% 45.924
16.92% 12.239
-21.95% 15.760
31.34% 37.690
-4.11% 15.565
-0.76% 12.074
0.63% 45.525
-0.87% 11.154
-8.87% 15.894
0.85% 34.730
-11.64% 15.345
-2.15% 12.216
1.82% 42.595
-7.25% 11.006
-10.07% 16.070
1.96% 35.387
-9.97% 15.399
-1.81% 12.181
1.52% 43.246
-5.83% 11.041
-9.79% 16.028
1.70% 33.516
-14.73% 15.281
-2.56% 12.258
2.17% 41.383
-9.89% 10.964
-10.42% 16.121
2.29% ENGIN OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS CHEMS OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS FOODS OUTPUT ($BILLION) % CHANGE OUTPUT APPAR OUTPUT ($BILLION) % CHANGE OUTPUT MOTOR OUTPUT ($BILLION) % CHANGE OUTPUT 40.468
0.05% 9.049
0.02% 13.836
-0.03% 30.284
0.22% 39.303
0.01% 2.090
-0.01% 92.958
0.14% 25.951
0.20% 18.224
0.23% 40.422
-0.06% 9.062
0.16% 13.815
-0.18% 29.951
-0.88% 39.408
0.28% 2.084
-0.30% 91.080
-1.88% 25.313
-2.27% 18.151
-0.18% 40.582
0.28% 9.081
0.35% 13.782
-0.39% 30.648
1.20% 39.294
-0.02% 2.091
0.02% 95.113
2.32% 26.497
2.10% 18.161
-0.35% 40.538
0.29% 9.096
0.37% 13.759
-0.40% 30.394
1.48% 39.393
-0.04% 2.085
0.04% 93.388
2.53% 25.919
2.39% 18.103
-0.26% 40.632
0.41% 9.111
0.68% 13.733
-0.74% 31.310
3.39% 39.280
-0.06% 2.092
0.06% 99.279
6.80% 27.631
6.47% 18.077
-0.81% 40.599
0.44% 9.123
0.67% 13.713
-0.74% 31.066
3.72% 39.375
-0.08% 2.086
0.09% 97.496
7.04% 27.045
6.85% 18.024
-0.70% 40.673
0.51% 9.158
1.20% 13.656
-1.30% 32.065
5.88% 39.358
0.14% 2.087
-0.16% 98.450
5.91% 27.133
4.55% 18.014
-1.15% 40.634
0.52% 9.172
1.21% 13.633
-1.31% 32.010
6.88% 39.460
0.13% 2.081
-0.14% 96.681
6.15% 26.551
4.89% 17.959
-1.06% 40.805
0.83% 9.248
2.20% 13.509
-2.36% 31.668
4.57% 39.418
0.29% 2.084
-0.32% 101.353
9.03% 27.509
6.00% 17.855
-2.02% 40.775
0.87% 9.260
2.18% 13.490
-2.35% 31.429
4.93% 39.512
0.26% 2.078
-0.29% 99.545
9.29% 26.920
6.35% 17.805
-1.90%
41

Another way to reduce petroleum use, and thus energy dependence, is to raise the price of petroleum. The table below compares select output for runs with an additional 20% state sales tax on PETRO (gray columns) with base runs (white columns) of E-DRAM. Imposing such a tax reduces state output by 0.6-0.7% and state income by 0.4-0.6%. It increases the price of CFUEL 4.7-6.0% while reducing domestic PETRO production 4.9-17.0% and domestic ENMIN production 3.7-6.7%. Unlike fuel efficiency strategies, the tax raises the price of vehicle miles traveled and thus does not generate cost savings that can be shifted to other sectors. Output across all sectors thus contracts slightly as the tax is basically inflationary.
42

CA OUTPUT ($BIL.) % CHNGE OUTPUT PERS. INC. ($BIL.) % CHNGE PERS. INC.
PRICE OF CFOOD PRICE OF CHOME PRICE OF CFUEL PRICE OF CFURN PRICE OF CCLOTH PRICE OF CTRANS PRICE OF CMED PRICE OF CAMUS PRICE OF COTHR ENMIN OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS PETRO OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS ENGIN OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS CHEMS OUTPUT ($BILLION) % CHANGE OUTPUT IMPORTS ($BILLION) % CHANGE IMPORTS EXPORTS ($BILLION) % CHANGE EXPORTS FOODS OUTPUT ($BILLION) % CHANGE OUTPUT APPAR OUTPUT ($BILLION) % CHANGE OUTPUT MOTOR OUTPUT ($BILLION) % CHANGE OUTPUT 1999 2020 2050 BASE MODEL TAX BASE MODEL TAX BASE MODEL TAX 1378.090 1367.183 3078.022 3057.935 6568.573 6532.449
0.08% -0.71% 0.10% -0.65% 0.11% -0.55% 892.489
886.188 2009.537 1998.180 4325.233 4306.451
0.09% 1.000
1.000
1.000
1.000
1.000
-0.62% 1.001
1.001
1.060
1.001
1.001
0.11% 1.000
1.000
1.000
1.000
1.000
-0.57% 1.001
1.001
1.054
1.001
1.001
0.12% 1.000
1.000
1.000
1.000
1.000
-0.43% 1.001
1.001
1.047
1.001
1.001
1.000
1.000
1.000
1.000
1.002
1.001
1.001
1.001
1.000
1.000
1.000
1.000
1.002
1.001
1.001
1.001
1.000
1.000
1.000
1.000
1.002
1.001
1.001
1.001
5.879
0.09% 17.540
0.05% 0.437
-0.06% 24.816
0.06% 2.806
0.01% 6.475
-0.01% 17.984
0.06% 4.028
0.01% 6.145
-0.01% 13.479
0.19% 17.534
0.00% 0.899
0.00% 41.240
0.11% 11.517
0.14% 8.051
0.20% 5.659
-3.66% 17.283
-1.42% 0.445
1.58% 23.594
-4.87% 2.854
1.74% 6.354
-1.88% 17.900
-0.41% 4.036
0.21% 6.131
-0.23% 12.875
-4.30% 17.618
0.48% 0.894
-0.53% 39.120
-5.04% 10.757
-6.47% 7.921
-1.42% 6.209
0.08% 36.011
0.07% 1.096
-0.07% 39.305
0.07% 15.683
0.01% 11.998
-0.02% 40.468
0.05% 9.049
0.02% 13.836
-0.03% 30.284
0.22% 39.303
0.01% 2.090
-0.01% 92.958
0.14% 25.951
0.20% 18.224
0.23% 5.912
-4.78% 35.243
-2.13% 1.123
2.40% 36.471
-7.21% 15.942
1.65% 11.784
-1.79% 40.313
-0.38% 9.068
0.20% 13.805
-0.22% 29.100
-3.91% 39.477
0.44% 2.080
-0.49% 88.711
-4.57% 24.451
-5.78% 17.985
-1.31% 7.689
0.07% 57.409
0.08% 2.640
-0.09% 39.254
0.11% 63.637
0.02% 19.142
-0.02% 87.033
0.05% 19.450
0.04% 29.741
-0.05% 64.994
0.24% 84.214
0.02% 4.650
-0.02% 200.230
0.17% 55.881
0.25% 39.348
0.24% 7.174
-6.69% 55.420
-3.47% 2.744
3.96% 32.592
-16.97% 64.399
1.20% 18.893
-1.30% 86.761
-0.31% 19.486
0.19% 29.679
-0.21% 62.797
-3.38% 84.553
0.40% 4.630
-0.44% 192.362
-3.93% 53.134
-4.92% 38.929
-1.07%
43

For comparison's sake, a Pigouvian tax levied on industries in proportion to their nitrogen oxide (NOX) emissions is briefly considered. Summary results of experiments run using the 1999 model with taxes set such that economy-wide NOX emissions are reduced by 5%, 10%, and 15% are reported below. The table indicates that achieving 5%, 10%, and 15% reductions via such taxation scheme would cause state product to drop 0.9%, 2.0%, and 3.2% respectively while shrinking state personal income by 0.7%, 1.6%, and 2.6% respectively. 1999 CA OUTPUT ($BILLION) % CHANGE CA OUTPUT CA PERSONAL INCOME ($BILLION) % CHANGE CA PERS. INC.
GENERAL FUND REVENUE ($BILLION) BASE MODEL 1378.0905
0.08% 892.4894
0.09% 56.7748
5% NOX CUT 1364.4467
-0.91% 885.4017
-0.71% 60.5554
10% NOX CUT 1349.8422
-1.97% 877.4866
-1.59% 64.3181
15% NOX CUT 1333.2856
-3.18% 868.1903
-2.64% 68.2828
The UC Berkeley team analyzed the economic impacts of four alternate strategies for reducing California's petroleum dependence. The strategies (summarized in Appendix C) were developed in a collaborative process between ARB, CEC, and ADL. Each scenario is built around two elements: (1) reduced gasoline demand from improved light-duty vehicle fuel economy, and (2) diesel fuel displacement from gas-to liquid (GTL) or Fischer Tropsch diesel fuels. The scenarios were constructed to try to “bound” the possible impacts to the California economy. Scenario 1 combines off-the-shelf fuel efficiency improvements in light-duty vehicles with a 33 percent blend of FTD in diesel fuel to meet ARB’s future ULSD specification. Scenarios 2 through 4 incorporate progressively aggressive and therefore more costly fuel efficiency and/or displacement options. The analysis uses E-DRAM, a modified version of the Dynamic Revenue Analysis Model used by the California Department of Finance. The analysis concludes that the statewide economic impacts of the strategies being considered are small. This is not surprising, given that static costs estimates of the most aggressive scenario under consideration are $13.7 billion in 2020, a time when gross state product (GSP) is projected to be nearly $3.1 trillion, and $22.1 billion in 2050, when GSP is projected to be nearly $6.6 trillion. The highest static cost estimates are thus only 0.33-0.44% of projected GSP. Results for the most modest and aggressive scenarios are summarized below as bounding cases. As indicated above, E-DRAM predicts that general equilibrium effects on state output and income are small. Predicted impacts on petroleum refining and crude oil production sectors are much larger, and should be interpreted as worst-case effects given the E-DRAM's weakness in allocating domestic demand reductions between domestic and imported products. Scenario 1, which embodies the most modest fuel economy improvements, may cause state gross product (GSP) and state personal income (SPI) to be slightly lower than would otherwise be the case. E-DRAM predicts Scenario 1 lowering 2020 GSP by 0.10% – a magnitude within the bounds of model calibration error, and 2050 GSP by 0.17%. The scenario's predicted effect on state personal income is essentially zero in 2020 and 0.10% (again, a magnitude within the bounds of calibration error) in 2050. Impacts on the directly effected sectors – crude oil producers (ENMIN) and petroleum refiners (PETRO) – are significant. E-DRAM predicts ENMIN and PETRO output falling 5.9% and 16.8% respectively.
47 Declines in these sectors, triggered by fuel efficiency gains, are offset by fuel cost savings being spent in other sectors. Scenario 4, which embodies the most aggressive change, has a modest impact on GSP and a marginal effect on SPI. E-DRAM predicts Scenario 4 lowering 2020 GSP by roughly 0.50%, and 2050 GSP by 0.46%.
44

The scenario's predicted effects on SPI are -0.42% in 2020 and –0.46% in 2050. As expected, the predicted impacts of this scenario on energy related sectors are large. E-DRAM predicts ENMIN output falling 9.67% in 2020 and 12.57% in 2050. PETRO output is projected to fall 14.73% in 2020 and 32.6% in 2050. Again, reduced spending in these sectors is displaced to others. The above results are robust to the sensitivity analyses performed. The model responds as expected to changes in the own-price elasticity of consumer demand for fuel, import elasticity, and prices. Sensitivity analysis confirms intuition that the scenarios under consideration become more attractive as world energy prices rise. Higher world energy prices simultaneous raise the consumer benefits of fuel efficiency while offsetting domestic energy producer costs by favoring domestic over imported fuel products.
45